Adolescent Victimization and Delinquent Behavior

Criminal Justice Recent Scholarship Edited by Marilyn McShane and Frank P. Williams III A Series from LFB Scholarly ...

Author: Erika Harrell

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Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

This page intentionally left blank

Adolescent Victimization and Delinquent Behavior

Erika Harrell

LFB Scholarly Publishing LLC New York 2007

Copyright © 2007 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Harrell, Erika, 1976Adolescent victimization and delinquent behavior / Erika Harrell. p. cm. -- (Criminal justice : recent scholarship) Includes bibliographical references and index. ISBN 1-59332-206-2 (alk. paper) 1. Juvenile delinquency--United States. 2. Abused teenagers--United States. 3. Criminal behavior--United States. I. Title. HV9104.H27 2007 364.360973--dc22 2006102799

ISBN-10 1593322062 ISBN-13 9781593322069 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

Table of Contents

List of Tables ......................................................................................vii Note to Readers ...................................................................................xi CHAPTER 1: Introduction .................................................................1 Research Questions .......................................................................3 CHAPTER 2: Agnew’s General Strain Theory ................................5 Background of General Strain Theory ..........................................6 Agnew’s General Strain Theory....................................................9 Conceptual Overlap of GST with Other Criminological Theories ..............................................................................13 The Place of Constraining/Conditioning Factors in General Strain Theory ......................................................................15 The Application of GST to the Victimization/Delinquency Relationship ........................................................................17 General Strain Theory and Gender and Racial Differences ........18 CHAPTER 3: Review of Research on Victimization and Delinquency ...............................................................................21 Childhood Victimization and General Delinquency....................23 Adolescent Victimization and General Delinquency ..................25 Violent Offending .......................................................................27 Drug Use .....................................................................................29 Nonviolent, Non-Drug Offending ...............................................33 Race and Gender Differences......................................................36 Hypotheses ..................................................................................39 CHAPTER 4: The National Youth Survey and Growth Curve Modeling.....................................................................................41 Data 41 Data Limitations..........................................................................44 Dependent Variables ...................................................................44 Independent Variables.................................................................53 Reliability and the Effect of Missing Data ..................................69 v

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Table of Contents

Analytical Techniques.................................................................72 Analysis Plan...............................................................................79 CHAPTER 5: Adolescent Victimization and Violent Offending ...85 Full Panel ....................................................................................85 African Americans ......................................................................89 Whites .........................................................................................92 Males ...........................................................................................95 Females .......................................................................................98 Racial Comparisons................................................................... 101 Gender Comparisons ................................................................. 102 CHAPTER 6: Adolescent Victimization and Illicit Drug Use...... 103 Full Panel .................................................................................. 103 African Americans .................................................................... 106 Whites ....................................................................................... 110 Males ......................................................................................... 113 Females ..................................................................................... 116 Racial Comparisons................................................................... 119 Gender Comparisons ................................................................. 120 CHAPTER 7: Adolescent Victimization and Nonviolent, NonDrug Offending........................................................................ 121 Full Panel .................................................................................. 121 African Americans .................................................................... 124 Whites ....................................................................................... 128 Males ......................................................................................... 131 Females ..................................................................................... 134 Racial Comparisons................................................................... 137 Gender Comparisons ................................................................. 137 CHAPTER 8: Can GST Explain the Relationship Between Adolescent Victimization and Delinquency?......................... 139 Returning to the Research Questions and Hypotheses .............. 140 Limitations of Study.................................................................. 146 Directions for Future Research.................................................. 147 Policy and Theoretical Implications.......................................... 149 Endnotes ........................................................................................... 151 Bibliography ..................................................................................... 153 Appendix: Missing Data Analyses .................................................. 163 Index.................................................................................................. 165

List of Tables

Table 4.1. Means, standard deviations, and variances for violent offending .................................................................................. 47 Table 4.2. Means, standard deviations and variances for nonviolent, non-drug offending................................................ 50 Table 4.3. Means, standard deviations, and variances for illicit drug use .................................................................................... 53 Table 4.4. Means, standard deviations, and variances for victimization............................................................................. 56 Table 4.5. Means, standard deviations, and variances for conforming values .................................................................... 59 Table 4.6. Means, standard deviations and variances for goals ........... 60 Table 4.7. Means, standard deviations, and variances for social support...................................................................................... 62 Table 4.8. Means, standard deviations, and variances for delinquent peers........................................................................ 65 Table 4.9. Percentages for race, gender and SES................................. 66 Table 4.10. Means, standard deviations, and variances for age ........... 67 Table 4.11. Cronbach’s Alphas for scales measuring conditioning factors at Wave 1...................................................................... 70 Table 4.12. Cronbach’s Alphas for scales measuring victimization, nonviolent/non-drug offending and illicit drug use at Wave 1 ................................................................... 71 Table 5.1. Random coefficient regression and control models predicting violent offending, full panel, coefficients (standard errors) ....................................................................... 86

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viii

List of Tables

Table 5.2. Models with interaction terms predicting violent offending, full panel, coefficients (standard errors) ................. 88 Table 5.3. Random coefficient regression and control models predicting violent offending, African Americans, coefficients (standard errors) .................................................... 90 Table 5.4. Models with interaction terms predicting violent offending, African Americans, coefficients (standard errors) ....................................................................................... 91 Table 5.5. Random coefficient regression and control models predicting violent offending, Whites, coefficients (standard errors) ....................................................................................... 93 Table 5.6. Models with interaction terms predicting violent offending, Whites, coefficients (standard errors) ..................... 94 Table 5.7. Random coefficient regression and control models predicting violent offending, males, coefficients (standard errors) ....................................................................................... 96 Table 5.8. Models with interaction terms predicting violent offending, males, coefficients (standard errors) ....................... 97 Table 5.9. Random coefficient regression and control models predicting violent offending, females, coefficients (standard errors) ....................................................................... 99 Table 5.10. Models with interaction terms predicting violent offending, females, coefficients (standard errors) .................. 100 Table 6.1. Random coefficient regression and control models predicting illicit drug use, full panel, coefficients (standard errors) ..................................................................................... 104 Table 6.2. Models with interaction terms predicting illicit drug use, full panel, coefficients (standard errors).......................... 105 Table 6.3. Random coefficient regression and control models predicting illicit drug use, African Americans, coefficients (standard errors) ..................................................................... 108 Table 6.4. Models with interaction terms predicting illicit drug use, African Americans, coefficients (standard errors) .......... 109 Table 6.5. Random coefficient regression and control models predicting illicit drug use, Whites, coefficients (standard errors) ..................................................................................... 111

List of Tables

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Table 6.6. Models with interaction terms predicting illicit drug use, Whites, coefficients (standard errors) ............................. 112 Table 6.7. Random coefficient regression and control models predicting illicit drug use, males, coefficients (standard errors) ..................................................................................... 114 Table 6.8. Models with interaction terms predicting illicit drug use, males, coefficients (standard errors) ............................... 115 Table 6.9. Random coefficient regression and control models predicting illicit drug use, females, coefficients (standard errors) ..................................................................................... 117 Table 6.10. Models with interaction terms predicting illicit drug use, females, coefficients (standard errors) ............................ 118 Table 7.1. Random coefficient regression and control models predicting nonviolent, non-drug offending, full panel, coefficients (standard errors) .................................................. 123 Table 7.2. Models with interaction terms predicting nonviolent, non-drug offending, full panel, coefficients (standard errors) ..................................................................................... 124 Table 7.3. Random coefficient regression and control models predicting nonviolent, non-drug offending, African Americans, coefficients (standard errors)............................... 126 Table 7.4. Models with interaction terms predicting nonviolent, non-drug offending, African Americans, coefficients (standard errors) ..................................................................... 127 Table 7.5. Random coefficient regression and control models predicting nonviolent, non-drug offending, Whites, coefficients (standard errors) .................................................. 129 Table 7.6. Models with interaction terms predicting nonviolent, non-drug offending, Whites, coefficients (standard errors).... 130 Table 7.7. Random coefficient regression and control models predicting nonviolent, non-drug offending, males, coefficients (standard errors) .................................................. 132 Table 7.8. Models with interaction terms predicting nonviolent, non-drug offending, males, coefficients (standard errors)...... 133

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List of Tables

Table 7.9. Random coefficient regression and control models predicting nonviolent, non-drug offending, females, coefficients (standard errors) .................................................. 135 Table 7.10. Models with interaction terms predicting nonviolent, non-drug offending, females, coefficients (standard errors)... 136 Table 8.1. Significance of victimization in explaining delinquency for all models.......................................................................... 139

Note to Readers

The points and views expressed in this publication do not necessarily reflect the views of the Bureau of Justice Statistics or that of the U. S. Department of Justice.

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CHAPTER 1

Introduction

Research examining the effect of being victimized prior to adulthood and subsequent delinquent and criminal behavior has been prominent in the research literature for several decades. The majority of this literature focuses on the effect of childhood victimization on subsequent delinquent and criminal behavior (Kruttschnitt and Dornfeld 1993; Maxfield and Widom 1996; McCord 1983; Rivera and Widom 1990; Smith and Thornberry 1995; Widom 1989a; Zingraff et al. 1993; Zingraff et al. 1994). Only since the late seventies and early eighties has research emerged looking at the effect of adolescent maltreatment on participation in criminal activities (Brezina 1998; Cleary 2000; Doueck et al. 1987; Fagan 2003; Katz 2000; Menard 2002; Paperny and Deisher 1983). The majority of the research on the effect of victimization on delinquency suggests that both childhood and adolescent victimization could increase the likelihood of participating in delinquent and criminal behavior (Brezina 1998; Cleary 2000; McCord 1983; Menard 2002; Zingraff 1993). However, not all of those who are victimized become delinquent. For example, Widom (1989a) found that while a significant portion of delinquents have been victimized, not all of those who are victims turn to criminal activity. Studying the effect of victimization during the adolescent years may be just as important as, and maybe more important than, studying the effect of childhood maltreatment. This is because some scholars have found differential effects of childhood and adolescent victimization on criminal behavior. In fact, Ireland et al. (2002) and Thornberry et al. (2001) have recently found that adolescent maltreatment was more predictive of delinquent behavior than childhood maltreatment. In order to determine the mechanisms that lead a victimized adolescent into delinquency, it is important to ground the analyses in 1

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theory. General strain theory is a criminological theory that has been used recently by several scholars in testing the victimization/ delinquency relationship and has received some empirical support (Agnew 2002; Agnew and White 1992; Brezina 1998; Katz 2000). There were three purposes of the present study. The first purpose was to apply Agnew’s general strain theory (GST) to the relationship between adolescent victimization and delinquent behavior using the first four waves of the National Youth Survey1 (1976-1979). The second purpose of this study was to examine the effect of these types of victimization on three categories of delinquency: illicit drug use, violent offending and nonviolent, non-drug offending. And finally, this study provided race and gender specific analyses to determine the efficacy of GST in explaining the relationship between victimization and delinquency across these subgroups of the population. This study contributed to the research literature in several ways. One, it used theory to guide the analysis. In the maltreatment literature, a significant proportion of studies simply insist that there is a connection between victimization and engaging in delinquency without specifying a theory that outlines the mechanisms that lead a victimized individual into delinquency (Kruttschnitt and Dornfeld 1993; Maxfield and Widom 1996; McCord 1983; Widom 1989a). Within the literature looking at the effect of adolescent victimization on delinquency, there have been only a few studies that have sought to test theory (Brezina 1998). Only if the mechanisms that lead a victimized adolescent into delinquent behavior are specified can policy be effectively guided to ameliorate the consequences of this victimization and prevent a victimized adolescent from engaging in delinquent behavior. A second contribution of the present study is that it examined racial and gender differences in this relationship. Very few studies of the childhood victimization/delinquency relationship have considered these group differences in their analyses, (McCord 1983; Menard 2002; Smith and Thornberry 1995). This is primarily due to a lack of diversity in their samples. The literature on adolescent maltreatment does not consider group differences at all.2 It is important to determine whether victimization has differential outcomes by race and gender. If differences exist, child protection agencies and other entities that deal with victimized adolescents could use the results of the present study to better help their clients according to the race and gender of their victims. Also, GST does provide a theoretical rationale, to be described

Introduction

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later, that predicts such racial and gender differences (Broidy and Agnew 1997; Eitle and Turner 2003; Mazerolle 1998). This research also adds to the literature by using data taken from a national probability sample of U.S. adolescents. The results are therefore more representative of all adolescents compared to studies that used either institutional samples, such as individuals in juvenile detention centers, or drug treatment facilities or convenience samples taken from the files of child protection agencies and police records (Dembo et al. 1987; Dembo et al. 1989; Dembo et al. 2000; Edwall et al. 1989; Harrison et al. 1989; Kaufman and Widom 1989). The generalizability of studies using convenience or institutional samples is limited to similar populations. However, using a national probability sample of adolescents will allow the results of this study to be generalized to the general adolescent population. And finally, this study used longitudinal data to examine the relationship between adolescent victimization and criminal behavior. There is a paucity of research that has utilized longitudinal data to test the effect of any type of victimization on criminal behavior (Katz 2000; Menard 2002; Thornberry et al. 2001; Widom 1989a).

RESEARCH QUESTIONS The specific research questions that were addressed in this study include the following: 1. Can Agnew’s general strain theory (GST) explain the relationship between adolescent victimization and delinquency? 2. Can GST explain the relationship between adolescent victimization and violent offending? 3. Can GST explain the relationship between adolescent victimization and nonviolent/non-drug offending? 4. Can GST explain the relationship between adolescent victimization and illicit drug use? 5. How do the relationships posited in questions 2 through 4 vary according to race? 6. How do the relationships posited in questions 2 through 4 vary according to gender?


CHAPTER 2

Agnew’s General Strain Theory

The notion of the “cycle of violence” or the “intergenerational transmission of violence” is an idea that has been associated with the child maltreatment/delinquency relationship for decades. However, some scholars are hesitant to call it a theory. Similar to social learning theory, which will be explained later, this idea stated that an individual who is a victim of violence learns that reacting violently is appropriate behavior. Later in life, this person will be prone to participate in violent behavior. It has been used often in explaining the relationship between maltreatment and criminal (especially violent) behavior committed later in life (Widom 1989a). The notion of the “cycle of violence” has permeated not only the academic field but society’s conscience as well. So much so that most do not question the notion that “violence begets violence.” However, Widom’s (1989b) review of then current empirical research of the “cycle of violence” found that support of this idea must be viewed with caution. Due to numerous methodological problems with studies supporting this idea, the hypothesis that violence can be transmitted from one generation to the next may not even be supported. In the literature focused on the effect of adolescent maltreatment on delinquent behavior, Brezina (1998) has argued that there are those who have used criminological theory, in lieu of the “cycle of violence explanation” to explain the effect of victimization in adolescence on criminal behavior though this research has not been very prominent. Authors who have used criminological theory have been at least somewhat successful in using these theories to explain how adolescent victimization can lead to criminal behavior (Paperny and Deisher 1983; Straus 1991). One of the theories that have more recently been used to explain the effect of adolescent victimization on subsequent delinquent behavior is general strain theory (Agnew and White 1992; Katz 2000). 5

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BACKGROUND OF GENERAL STRAIN THEORY Strain theory is not a new concept. Its roots can be traced to the work of Emile Durkheim and his idea of anomie. Durkheim (1951) argued that individuals have insatiable and ever increasing desires that will be boundless if left unchecked. These desires must be restricted and held at a level where fulfillment is possible or people will suffer from permanent sadness throughout their lives. Durkheim assumed that left to themselves, individuals would not voluntarily restrict their desires. Therefore, an external entity must apply some form of regulatory force to set limits for these insatiable desires. Durkheim noted that society is the only entity that has the power to implement laws and stipulate societal norms that control these limitless desires. Durkheim (1951) stated that under most conditions, societal laws and norms are clear and people do abide by them. However, during societal upheaval, the limits imposed by society may become unclear. This condition is what Durkheim called anomie or a state of normlessness. As anomie increases, so does pathological behavior. He stated that more developed countries are more susceptible to anomie than underdeveloped countries due to increased social mobility and recurring changes in the economy, which contribute to less enduring norms. Robert Merton (1938) extended Durkheim’s work by more specifically delineating the sources of anomie and also by applying anomie to American society. Merton argued that anomie emerges when there is a disparity of emphasis on the importance of obtaining societal goals and the legitimate means for reaching these goals. He stated that when some portions of the society (such as the poor) are constantly informed that material rewards and monetary success are goals that are highly valued, and they are also told that they can not access the legitimate means for achieving these goals, the result will be anomie. As a result of this discrepancy between the goals and the means, those segments of society that still maintain a desire to achieve the societal goals may ultimately reject legitimate means in favor of other means to achieve these goals. An example of Merton’s ideas can be seen by examining a poor, inner-city neighborhood. People who live in such areas are exposed to society’s values regarding success in the form of monetary success and obtaining expensive material goods. These same people often find themselves closed off from legitimate means to obtain these societal


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goals. Examples of this blockage can be seen in the lack of legal employment and schools offering quality education. Both means could help those to reach societal goals. In response to this blockage, some individuals in poor, inner-city neighborhoods may turn to other, sometimes illegal, means of obtaining society’s definitions of success. These alternative means can include activity such as selling illegal drugs. It is this disjunction between societal goals and access to legitimate means of achieving such goals that can help to explain high amounts of crime in poor neighborhoods. Merton (1938, 1957) argued that there are five ways in which an individual may adapt to anomie. Conformity is acting in accordance with both societal goals and institutionalized means. For example, someone who gets a college degree to obtain a successful career is a conformist. In innovation, a person accepts society’s goals but rejects legitimate means to achieve these goals. For example, an innovator may be a person who steals money from a bank to buy an expensive house. Ritualism is where an individual rejects cultural goals but accepts institutionalized means. A person who stays in a dead-end, low paying job for years without any desire to pursue a better position is someone who has become a ritualist. An individual engages in retreatism when she/he rejects both societal goals and the legitimate means to obtain them. A drug addict, alcoholic, or vagabond could fit into this category. Finally, rebellion is where an individual seeks to redefine societal goals and institutionalized means. For example, in our society, an individual who adapts in this way may join an organization that supports changes to cultural standards for societal goals and means. Richard Cloward (1959) extended the work of Merton and Durkheim. He argued that in Merton’s work, the idea that those who do not have access to legitimate means are likely to turn to illegitimate means to achieve societal goals is based on the assumption that illegitimate opportunities are always available to everyone. Cloward contended that this might not always be the case. He noted that there are “differentials in availability of illegitimate means” (p. 167). In other words, just as there are different legal opportunity structures, there are different illegal opportunity structures as well with different types of access to these illegal opportunity structures. An individual who is blocked from legitimate means of achieving societal goals will turn to the illegitimate opportunities that are most readily available.

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The availability of illegitimate means is determined by several factors, similar to those of legitimate opportunity structure (Cloward 1959). One factor affecting availability is one’s location in the social structure. Another is the acquisition of the values and skills associated with the illegitimate opportunity structure. Also included is having the chance to use illegitimate means to obtain societal goals. An example of Cloward’s claims can be shown by looking at a person from a poor neighborhood who turned to robbing others after being unable to find employment where he or she lived. The neighborhood that the person lives in may not have opportunities for obtaining legal employment. If that person has the values favorable towards robbing people and has learned to commit such acts, according to Cloward’s ideas, then he or she is more likely to turn to robbing people to obtain the societal goal of monetary rewards. A few years after Cloward’s research, Albert Cohen (1965) further criticized Merton’s work, by arguing that it was atomistic and individualistic. He stated that Merton’s theory of anomie did not take into consideration how the activities of others can affect an individual’s choice of adaptation to anomie. For example, an adolescent male wishes to buy expensive clothing that is valued in society and is blocked from this goal due to lack of available funds in his family. He has several friends whom he sees are able to purchase expensive clothing using money gained from selling illegal drugs in their neighborhood. Seeing the success of friends in obtaining expensive clothing, he may be more likely to turn to selling drugs in order to achieve this goal. Also, Cohen (1965) did not agree with Merton’s assumption that all people in society internalized dominant societal values at the same level. Cohen thought there were differential levels of internalization of societal goals that could lead to individuals to deal with anomie in different ways. Therefore, two people may be blocked from means to societal success. One person who has internalized society’s emphasis on owning an expensive car may resort to stealing one in order to deal with the disjunction of goals and means. The other person who has not internalized this goal as much may be less likely to steal expensive cars. Furthermore, Cohen (1965) wrote that Merton’s anomie theory sees the change from conformity to deviance as a sudden change. Cohen extended anomie theory to include elements of role theory.


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Roles are positions in the social structure where disjunctions between goals and means are located and adaptations (innovation, retreatism, etc.) arise. Some roles involve an imbalance of goals and means and much deviant behavior comes about as a way of dealing with this imbalance. Therefore, an individual who is poor and part of the lower class in society is usually in a role or position in society where there is a disjunction between goals and means. To deal with this imbalance, the person might innovate by stealing cars or retreat by using drugs. Another important extension of anomie theory by Cohen (1955) was the recognition of non-instrumental deviance to resolve states of frustration. He argued after studying young males in gangs, that those in the working class might be unprepared to deal with middle-class institutions such as school. They may not have acquired the skills it takes to achieve middle class success. This may cause frustration to build up within a working class individual. In order to deal with this frustration, one may turn to non-instrumental or expressive deviance, such as assault. Non-instrumental deviance is different from instrumental deviance. Instrumental deviance is the result of being unable to obtain desired goods or services (i.e. theft).

AGNEW’S GENERAL STRAIN THEORY Building on each of these early theorists, Robert Agnew created a revised version of strain theory, which he has called “General Strain Theory.” In an early article (1985), he argued that individuals not only seek certain societal goals, as stated by Merton (1938, 1957) and Cloward (1959), but also try to avoid painful situations. Like legitimate avenues of achieving goals, efforts to avoid painful situations may also be blocked. This blockage of avoidance of pain may be frustrating and produces strain. This may, in turn, lead to illegal escape attempts or anger-based delinquency, which builds upon Cohen’s (1955) ideas regarding non-instrumental deviance. Agnew (1985) also contended that blockage of pain avoidance behavior is distinct but not irreconcilable with the blockage of goal seeking behavior. The two can coexist. For example, an adolescent who cannot escape the situation of being constantly beaten by parents that he or she lives with may be frustrated because the beatings may interfere with achievement of societal goals such as doing well in school.

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He saw both types of blockages (of pain avoidance behavior and of achieving societal goals) as sources of strain that can increase the likelihood of participation in delinquent behavior. His 1985 version of strain theory assumed that the blockage of pain avoidance behavior leads to delinquency directly and indirectly through anger. He argued that these direct and indirect paths to delinquency exist whether or not one is blocked from legitimate avenues of achieving societal goals. In 1992, Agnew advanced his theory by expanding the discussion of negative stimuli. Within negative relationships, an individual is not treated the way he or she wants to be treated. This treatment or negative stimuli causes the victim to feel negative affective emotions such as anger. One of the consequences of feeling these emotions or strain is that individuals who experience them may feel compelled to respond in some way to alleviate these feelings. There are several responses an individual may have to this strain, one of which is delinquent and criminal behavior. An example of Agnew’s ideas can be seen in an adolescent male who is constantly teased at school and later comes to school and shoots the classmates that are teasing him. The taunted youth is not treated the way he would like to be treated. The male may feel anger towards his peers who are the source of the teasing. Shooting his classmates could be seen as a way of responding to the anger that resulted from the taunting. This, of course, is an extreme example. Also in this later work, Agnew (1992) described three sources of strain, each describing a different type of negative relationship with other individuals. One source is where others prevent one from achieving positively valued goals. This is similar to the original conceptualization by Merton (1938). A second source of strain is where others remove or threaten to remove positively valued stimuli that one possesses. A parent withholding things (e.g. affection, toys) from an unruly child in an attempt to discipline him or her falls into this category. In the third source of strain comes from presenting or threatening to present one with negatively valued stimuli, as was mentioned in Agnew’s earlier revision of strain theory (1985). A parent physically abusing a child falls under this type of strain. The adaptations to strain that Agnew (1992) suggested are somewhat different than the five suggested by Merton (1938). Agnew (1992) contended that there are several categories of adaptations. One involves ignoring or minimizing the importance of the strain similar to


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Merton’s notion of retreatism. This entails reducing the significance of the goals or values that the strain affects. Another mode of adaptation is maximizing positive outcomes or minimizing negative outcomes. This may involve seeking positively valued goals not affected by the strain or escaping from negative relationships, such as focusing on participating in sports to escape from an abusive household. A third mode of adapting mentioned by Agnew (1992) is accepting responsibility for the strain by convincing yourself that you deserve the strain you experience. A wife who is beaten by her husband may justify the behavior by thinking that she deserved the abuse falls under this category. Another method of coping with strain is vengeful behavior. Here, the blame is placed on others for the strain and the victim seeks to retaliate to put an end to the strain. An example is a young girl attacking her father who had committed incest with her. Delinquent and criminal behavior is a possible adaptation to strain that can fall under several of the aforementioned types of adaptations. However, as Agnew (1992) pointed out, not everyone who experiences strain turns to illegal behavior as a response. There are several factors that influence the choice of adaptation for a person subject to strain. These factors are the constraints to and supports of nondelinquent and delinquent coping and the disposition to engage in nondelinquent versus delinquent coping. Constraints to nondelinquent and delinquent coping include one’s values, goals, and identities. If strain affects important goals, values, and identities and a person has few alternative legal goals, values, and identities to turn to, he or she will more likely turn to illegal behavior. For example, an adolescent who is abused at home by his or her parents and has no aspiration for school or work will be, according to Agnew, more likely to turn to delinquent or criminal coping. Also, individual coping resources like self-esteem, intelligence, and interpersonal skills are constraints to delinquent responses. Individuals under strain who are high in self-esteem and intelligence and have good interpersonal skills will be less likely to use criminal behavior as a response. Another constraint is what Agnew (1992) called conventional social support. Those with little support from family and friends will be more likely to turn to delinquency when strained. Moreover, the costs and benefits of participating in delinquent behavior in a particular situation are important as well. When the costs of delinquency exceed the benefits, delinquency is less likely to occur.

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In addition to individual constraints, Agnew also included the effects of societal constraints. The larger society offers constraints to delinquent versus nondelinquent coping for a victim of strain. Societies provide a constraint to delinquent coping by associating importance to certain goals and identities. For example, a person under strain might be tempted to drop out of school. If society values education as one of its goals, that person may be less likely to drop out of school and look at another avenue to respond to the strain (Agnew 1992). Aspects affecting the disposition to delinquency also include the extent to which delinquency was reinforced in the past. If delinquent behavior was not reinforced prior to experiencing strain, a person will be less likely to use delinquent coping to respond to strain. Also, an individual’s beliefs, especially beliefs regarding the suitable response to provocations, also make up one’s disposition to delinquency. A person who believes that most provocations should be handled with a violent reaction will be more likely to engage in violence when under strain. Whether one sees others as causing the strain also increases the likelihood of participating in delinquency as a response. Moreover, one’s disposition to delinquency is also related to one’s association with delinquent peers (Agnew 1992). The more delinquent peers one has, the more likely a person under strain will engage in delinquent behavior. Not only are there certain factors that lead a person subject to strain to a delinquent response but also certain types of strain are also more likely to lead to delinquent behavior compared to other strains. In a recent article (2001), Agnew contended that there were several types of strain are more likely to guide a person to a criminal response. One is strain that is seen by the victim as unjust. For example, a person is fired from a job and they feel that they should not have been fired. The person may go back to their former job and assault their former boss in retaliation for the dismissal. Another type is strain that is seen as high in magnitude. This includes strain that is lengthy in duration, high in frequency, and has a high negative degree such as severe physical injuries. Also, strain in the life of someone with low social control is more likely to lead to delinquent coping. This is because social control constrains the effect of strain on delinquent behavior. A fourth type of strain that has a higher likelihood of leading a victim of strain to crime as compared to other types of strain is strain that created pressure to


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engage in criminal coping. For example, certain types of strain deal with exposure to others who model criminal behavior. A victim may emulate this activity and respond to strain with crime (Agnew 2001).

CONCEPTUAL OVERLAP OF GST WITH OTHER CRIMINOLOGICAL THEORIES In the above section describing Agnew’s general strain theory, some elements of GST may seem to overlap with those of other criminological theories. More specifically, the theory shares aspects with two other criminological theories, in particular, social control theory and social learning theory. While Agnew (1992) maintained that GST was an original theory, he also acknowledged this overlap. Social control and social learning theories have been among the dominant criminological theories for decades. In Hirschi’s original conception of social control theory, he stated that when a person’s bonds to society (relationships with family, friends, etc.) are broken or weakened that person would be more likely to engage in delinquent behavior (Hirschi 1969). According to this theory, the fear of having breaches in these bonds causes people to abide by societal norms and laws. Hirschi argued that there are four bonds that keep a person from committing crime. They are: (1) attachment to others such as family members and institutions such as school and church, (2) commitment to the social and economic system, (3) involvement in legitimate activities that leaves one too little time to commit delinquent acts, and (4) belief in conventional norms and values. With social learning theory, Ronald Akers (1977) stated that people learn social behavior, including criminal behavior, through operant behavioral conditioning. Social behavior is learned through direct conditioning and/or modeling of others’ behavior. Modeling occurs when someone emulates and develops the same behavior of others with whom they identify most strongly. Behavior is reinforced when positive rewards are gained or punishment is avoided. Akers contended that whether criminal behavior is begun or continued depends on the degree to which it has been rewarded or punished and also how the alternatives to crime are rewarded and punished. General strain theory is similar to social learning and social control theories in several ways. First, all three theories are sociological theories and each explains delinquency in terms of the individual’s

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social relationships (Agnew 1992). Second, GST argues that several factors condition the effect of strain on an individual and provide constraints to delinquent coping. Agnew noted that one’s initial goals, values, and identities help to determine whether a victim of strain responds with illegal behavior. He argued that if strain affects important goals, values, or identities in a person who has few alternative goals, values, or identities to turn to, that person will be more likely to turn to delinquent coping. This notion of values in GST is similar to Hirschi’s (1969) idea about belief in conventional norms and values. Recall that Hirschi contended that when the bond of belief is broken, one is more likely to engage in delinquent behavior. Even though Agnew does not argue this, his notion of strain can be seen as a mechanism that breaks the bond of belief, which, in turn, can lead a strained individual to delinquency. A similarity between social learning and general strain theories can be seen in the type of relationships that each deal with. General strain theory deals with relationships where one is not treated the way he or she wishes to be treated. Social leaning theorists also contended that these relationships, such as relationships where a child is being sexually or physically abused by a family member, affect the likelihood of the child being abusive themselves. While both social learning theory and GST deem these as negative relationships that affect the probability of delinquency, each theory has somewhat different assumptions about why this is so. Social learning theory’s notion of a negative relationship comes from a person learning and modeling negative behavior of an important person in their life. In general strain theory, a negative relationship occurs when a person is not treated the way they would like to be treated and is acting out as a result of the emotions that arise from being in such a relationship. Another overlap of GST with social learning theory can be found in Agnew’s (1992) ideas of the factors that affect the disposition to delinquency. He argued that a person’s disposition to delinquency is affected by that individual’s association with delinquent peers. He stated that those who associate with delinquent peers are more likely to be exposed to and have delinquent beliefs reinforced on them. This notion of exposure and reinforcement of delinquent ideas is similar to the proposition offered by Akers (1977) in social learning theory.


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Furthermore, Agnew (2001) stated that some types of strain are more likely to be associated with being exposed to those who cope with strain by engaging in crime. By modeling and reinforcing this type of coping, an individual under strain may see crime as an available and appealing option to cope. This, of course, is similar to the modeling of behavior postulated by social learning theory. Agnew (2001) addressed theoretical overlaps by delineating more specifically the concepts of his theory and how they relate to those of social control and social learning. He argued that strains that are associated with low social control are more likely to result in crime. This combines Hirschi’s (1969) social control theory with GST. Agnew noted that certain strains such as inconsistent parental discipline (low direct control), parental rejection (low attachment), working on a job that makes little money (low commitment) all increase the likelihood of a person under strain to respond with illegal behavior. Even though general strain theory may overlap conceptually with other theories, Agnew (1992, 2001) insisted that it is a theory that is distinguished by two very important aspects. One is that GST focuses explicitly on negative relationships with others, relationships in which an individual is not treated how they would like to be treated. The other aspect is that general strain theory argues that individuals are pressured into delinquency by negative affective states such as anger that come from being in these negative relationships. These two aspects are unique to GST and set it apart from other criminological theories (Agnew 1992). Thus, although the concepts measured in this study may be similar to those from other theories, particularly social control and social learning theory, they are intended to be indicators of constructs derived from Agnew’s general strain theory.

THE PLACE OF CONSTRAINING/CONDITIONING FACTORS IN GENERAL STRAIN THEORY Even though Agnew’s general strain theory is a very good theory for explaining some of the mechanisms that may lead one to participate in criminal behavior, it is somewhat ambiguous regarding the process through which strain affects behavior. In 1992, Agnew explained that the effect of strain on delinquency is influenced by a variety of factors such as values of an individual and one’s disposition to delinquency. However, he did not specify how these factors influence the effect of

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strain. He simply stated that these factors condition or constrain the effect of strain. Whether he meant that these factors mediate the effect or if they affect strain in another way is not made clear. As a result of this omission, researchers testing general strain theory are left to interpret Agnew’s arguments on their own. Mazerolle and Maahs (2000) specifically tested Agnew’s notions regarding the influence of certain factors on strain. By using chi-square analyses of longitudinal and cross sectional data, they found that strain and delinquency were related in a linear pattern in that individuals who experienced more strain had higher levels of delinquent behavior. Also, they found that certain factors such as exposure to delinquent peers do influence the effect of strain both cross-sectionally and longitudinally. The majority of other empirical tests of Agnew’s general strain theory have modeled measures of delinquency as being the function of strain, demographic variables, and factors that have been said to constrain the effect of strain (Agnew and White 1992; Baron 2004; Eitle and Turner 2003; Katz 2000; Mazerolle et al. 2000). In other words, the effect of strain on delinquency was controlled by demographic variables and constraining and conditioning factors. These studies have all found at least partial support for GST. In fact, Agnew himself tested GST by modeling delinquency as a function of strain and other conditioning factors, allowing strain’s influence to be controlled by conditioning factors. For example, using a sample of adolescents, Agnew and White (1992) regressed measures of delinquency and drug use on variables representing strain and several of the factors believed to condition the effect of strain. In doing so, Agnew and White found partial support for general strain theory. More recently, Agnew (2002) tested his theory, with a sample of adolescent boys, by modeling the effect of strain on delinquency as being controlled by constraining and conditioning factors such as delinquent peers and demographic variables. He too found support for GST. Likewise, Mazerolle (1998) tested some principles of GST using data from the National Youth Survey. He modeled delinquency as a function of measures of strain which were controlled by other variables representing such constructs as delinquent disposition and moral beliefs, some of the conditioning and constraining factors. He too found some support for GST. Not all scholars, however, agree that controlling strain with conditioning/constraining factors is the appropriate way to test GST.


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Hoffman and Miller (1998) argued that relying on these “control” models of strain by simply controlling for aspects such as family relationships might lead to model misspecification. Because Agnew (1992) argued that strain may weaken a youth’s attachment to conventional institutions, thereby attenuating the bond and increasing the likelihood of participating in delinquency, these authors contend that a mediating process should be modeled. Although a few scholars have used a mediation approach and found partial support for GST (Paternoster and Mazerolle 1994), this method has been the exception, rather than the rule. The present study used control models to assess how well GST can explain the victimization/delinquency relationship. It also attempted to assess longitudinally, like Mazerolle and Maahs (2000), whether the effect of strain is moderated by conditioning factors such delinquent peers by incorporating interaction terms into the models used. Each of these terms consisted of the interaction between strain and a conditioning factor. More information regarding these interaction terms is presented later.

THE APPLICATION OF GST TO THE VICTIMIZATION/ DELINQUENCY RELATIONSHIP As stated earlier, a significant proportion of the studies that have examined the childhood victimization/delinquency connection have used the cycle of violence explanation to explain findings. Among those studying the effect of adolescent victimization on subsequent criminal behavior, few have turned to criminological theory to explain the connection. Of the researchers that have used criminological theory, some have relied on Agnew’s general strain theory (Harrell 2001; Kakar 1996) even though they did not explicitly test propositions from GST. Others actually tested GST and found that it showed reasonable validity in explaining the effect of victimization on delinquency (Agnew and White 1992; Baron 2004; Brezina 1998; Katz 2000). For example, Agnew (2002) tested GST using a sample of adolescent boys and found that experienced, as well as vicarious physical victimization was related to delinquency. To apply GST to explain the relationship between adolescent victimization and subsequent delinquency, one would first assume that adolescent victimization is the presentation of negative stimuli. Various

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constraining factors such as the importance of societal goals of an individual would be hypothesized to condition the effect of victimization. If the constraining factors are not successful in keeping one from delinquent behavior, that person is more likely to participate in illegal activity. If the constraining factors are successful in keeping one from delinquent activity, then he or she will be less likely to engage in criminal conduct.

GENERAL STRAIN THEORY AND GENDER AND RACIAL DIFFERENCES Agnew’s general strain theory has also been applied to various group differences in crime, particularly gender and racial differences. In Broidy’s and Agnew’s (1997) theoretical article speculating on the application of GST to the gender gap in crime, they argued that males and females experience different types of strain, which leads to distinct behavioral outcomes. Males are more often subject to financial strain, which is conducive to property crime, and to severe interpersonal conflict, which is conducive to violent crime. The types of strain most commonly experienced by females involve levels of social control and a restriction of criminal opportunities (for example, the burden of family members) and may be more conducive to self-destructive forms of behavior such as drug use. Broidy and Agnew (1997) also noted that differences in male and female crime rates are partly a result of different emotional responses to strain. Anger can be experienced by both males and females in response to strain. However, females are more likely to experience depression, guilt and shame in addition to the anger. Such emotions lead to self-destructive types of crime such as drug use in females. Furthermore, they hypothesized that females are less likely than males to respond to strain with crime due to gender differences in social support and coping styles, opportunities for crime, social control and disposition to crime (Broidy and Agnew 1997). Mazerolle (1998) also examined if GST could explain the gender gap in crime. He found evidence of gender differences in the effects of strain on criminal behavior. Exposure to various life events such as losing a family member, and negative relations with adults increased the likelihood of criminal behavior for males but not for females. Also, he found that negative life events were an important predictor of


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property crime for males but not for females. These findings suggest that females may have more coping mechanisms for strain and/or females may respond with more inner-directed behavior such as illicit drug use. A few years later, Piquero and Sealock (2004) tested Broidy and Agnew’s claims and found some support for their notions about gender and GST. Piquero and Sealock discovered that for males, strain was significant in participating in interpersonal aggression and property offending, however, it was not a significant predictor for females for either type of offending Despite this exploration of the differential effects of GST in predicting delinquent behavior by gender, very little work has been done to explore the differences in its application across racial groups. There have been no theoretical articles applying GST concepts to the racial gap in offending behavior.2 However, there may be a reason to look at the effects of GST by race. Eitle and Turner (2003) hypothesized and found that racial differences in criminal participation are largely due to African Americans being exposed to more stressful events during their lifetimes than Whites. As such, African Americans can be assumed to experience more lifetime strain compared to their white counterparts, which also leads to more involvement in crime. Moreover, conditioning factors may differ for African Americans and Whites. Because of the paucity of research exploring the efficacy of GST in predicting delinquency for gender and race-specific groups, an important gap in the literature exists. Broidy and Agnew (1997), Mazerolle (1998), and Eitle and Turner (2003) each demonstrate that there may be mechanisms within the theory that can explain why males and African Americans are more likely to participate in criminal behavior. This information regarding racial and gender differences and GST provides conceptual rationale for the present study’s look into race and gender differences in applying GST to the adolescent victimization/delinquency relationship.


CHAPTER 3

Review of Research on Victimization and Delinquency

For several decades, there has been a plethora of research on the deleterious effects of childhood maltreatment. More recently, there has been an increase in the number of articles that examine the effects of maltreatment and victimization on subsequent delinquent and criminal behavior. Curtis (1963) helped to initiate this wave of research by noting that maltreated youth would “become tomorrow’s murderers and perpetrators of other crimes of violence, if they survive” (p. 386). Since that article, practitioners, researchers, and the public have all begun to embrace the notion that being victimized can cause one to become delinquent later. The majority of this research has examined the effect of childhood victimization on subsequent behavior. Only recently has the effect of victimization during the adolescent years been related to subsequent criminal behavior. This smaller literature, similar to work on the effect of childhood victimization, indicates that being victimized as an adolescent also increases the probability of engaging in criminal behavior (Brezina 1998; Ireland et al. 2002; Thornberry et al. 2001). In developmental psychology, childhood has been defined as the period from birth and ends at the beginning of puberty. It is a time when the individual is the most protected and is not usually permitted to participate in adult behavior. Furthermore, adolescence has been defined as a time of transition in which the developing child is permitted to try out adult roles in a more protected way. It is said to begin with puberty and to terminate when one assumes adult-like responsibilities on their own without supervision. Because individuals go through puberty and experience adult-like responsibilities at various 21

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ages, there is no clear particular year that defines when childhood stops and adolescence begins (Liebert et al. 1974). As a result, a variety of age cut-offs have been used to operationally define “adolescence” and “childhood” in the victimization/delinquency literature. In the literature on the effect of childhood victimization on delinquency, researchers have operationalized childhood victimization as victimization that occurred from birth up to and including ages 9 (Zingraff et al. 1993), 10 (Mouzakitis 1981; Rivera and Widom 1990; Widom and White 1997); 11 (Kaufman and Widom 1999; Maxfield and Widom 1996; Tyler and Johnson 2006; Widom 1989a; Widom 1989c; Widom et al. 1999) and 12 (Herrera and McClosky 2003; Kruttschnitt and Dornfield 1993; Smith and Thornberry 1995). Not surprisingly, in research claiming to look at the effect of adolescent victimization a few studies may look at victimization in youth whose ages may overlap with those who have operationalized these years as occurring in childhood. For example, Fagan (2003), Menard (2002), Mouzakitis (1981), and Shaffer and Rubach (2002) considered individuals aged 11 and 12 years old to be adolescents, while others defined adolescence as starting later (Agnew 1985; Agnew 1989; Agnew and White 1992; Bach and Anderson 1980; Brezina 1998; Cleary 2000; Fagan et al. 1987). Studies that compared the effects of childhood victimization to victimization in adolescence have more consensus as to when childhood ends and adolescence begins. Benda and Corwyn (2002), Ireland et al. (2002), Thornberry et al. (2001) and Vissing et al. (1991) all specified that abuse prior to and including age 11 was considered childhood victimization and victimization occurring from age 12 to age 17 was considered adolescent victimization. At the extreme, in the literature are some authors who consider victimization that occurs from birth all the way into the teen years and also into the early twenties as childhood victimization (Dembo et al. 1987; Dembo et al. 1989; Dembo et al. 2000; Finkelhor 1979; Katz 2000; Kruttschnitt and Dornfeld 1993; Lemmon 1999; Locke and Newcomb 2004; McCord 1979, 1983; Pollock et al. 1990; StouthamerLoeber et al. 2001). Still, others do not designate whether abuse occurred during childhood or adolescence and simply refer to the maltreatment as victimization (Durant et al. 1994; Edwall et al. 1989; Harrison et al. 1989; Harrison et al. 1997; Harrison and Hoffman 1989;


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Heck and Walsh 2000; Kilpatrick et al. 2000; Lewis et al. 1979; Perez 2000, 2001; Van Hasselt et al. 1992; Zingraff et al. 1994). Due to the lack of consensus in the research literature and the vague definitions of childhood and adolescence, it is up to the researcher to operationalize childhood and adolescent victimization. As such, conclusions about the effects of childhood and/or adolescent victimization on offending must be made aware of this issue. The literature review below uses the definitions provided by each respective study.

CHILDHOOD VICTIMIZATION AND GENERAL DELINQUENCY While most of the research literature over the years has acknowledged a link between childhood maltreatment and subsequent delinquency, there are some scholars who have challenged this research (Doerner 1987; Schwartz et al. 1994; Widom 1989b; Zingraff et al. 1993). They claim that various methodological flaws have caused much of this research to be unreliable. This, in turn, raises doubt about the finding that maltreatment increases the likelihood of participation in delinquent and criminal behavior. Doerner (1987) noted four major problems that permeated studies of childhood victimization and delinquency. One is that most studies did not differentiate between the types of maltreatment that were experienced (violent victimization, property victimization, etc.). A second problem was that research usually did not acknowledge the limitations of official versus self-report data. For example, relying solely on official data could leave out certain people not processed through either the child welfare or criminal justice systems. A third problem was not specifying the type of delinquency measured. And finally, the sample composition in the extant studies caused serious problems with generalizability. Results of studies using samples consisting solely on institutionalized individuals can only be applied to similar populations. Later reviews of the child maltreatment/delinquency literature by Widom (1989b) and Schwartz et al. (1994) leveled criticisms similar to those of Doerner (1987). They noted that the extant literature contained a lack of specificity in the delinquency and child maltreatment variables across studies. Different studies used different definitions of

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delinquency and maltreatment. The use of convenience samples from places such as juvenile detention centers, they argued, caused problems with generalizability. Also, they noted that the lack of control groups was a serious methodological flaw. McCord (1983) was one of the first to examine the relationship between childhood victimization and offending using a longitudinal, prospective research design. She studied the medical and social work records of 232 males who were separated into four groups: those who were “neglected,” “abused,” “rejected,” or “loved.” Abuse was defined as using corporal punishment on a regular basis to control the child. McCord (1983) found in a follow up of official records those who were labeled “abused” had juvenile delinquency than those who were labeled “loved.” She also noted that the self-confidence of the mother and mother’s education appeared to lessen the effect of maltreatment on the subjects. Six years later, Widom’s (1989a) widely cited study further supported McCord’s findings. Widom found that being abused or neglected as a child increased one’s risk for delinquency, adult criminal behavior and violent criminal behavior. In 1996, Maxfield and Widom reported similar findings as Widom. They found that childhood abuse had a significant impact on the likelihood of arrest for delinquency, adult criminality, and violence. Both Widom and Maxfield and Widom, like McCord, used official information such as adult and juvenile criminal court records. They also used a prospective, matched comparison-group design in which they followed a sample of abused and nonabused children through adulthood. Other recent prospective longitudinal studies support the maltreatment/offending link as well. Smith and Thornberry (1995) used official data from files of child protection agencies, police records and self-report data from a longitudinal study and discovered that a significant relationship exists between child maltreatment and selfreported and official delinquency, especially for serious delinquent behavior. Also, Lemmon (1999), using a prospective study with a comparison group, found that the presence of maltreatment significantly affected the initiation and continuation of delinquency. More recently, Tyler and Johnson (2006), in a longitudinal study of adolescents, used a combination of reports of caregivers and case workers and self reports to determine that both sexual and physical abuse in childhood could lead to delinquent behavior later.


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ADOLESCENT VICTIMIZATION AND GENERAL DELINQUENCY Turning to work on the effect of adolescent victimization on delinquency one finds that the majority of this research shows a link between victimization and delinquent behavior. For example, Mouzakitis (1981) found that out of a group of delinquent girls, over a quarter had been physically punished during their adolescent years. Most of this physical punishment resulted in abuse leaving scars, bleeding, and bruises. Also, Bach and Anderson (1980) studied the effects of adolescent sexual abuse and found that sexually abused adolescents were more likely to engage in behavior such as prostitution, running away, and other forms of delinquency compared to nonabused adolescents. A few years after the Bach and Anderson article was published, Paperny and Deisher (1983) conducted a literature review of the then current literature on the effect of adolescent maltreatment and exploitation and predisposition to commit offending behavior. They found that most studies showed that adolescents who committed various offenses had usually been victimized in various ways. In 1986, Garbarino and his colleagues argued that several types of delinquent behaviors in adolescence such as running away from home and substance abuse could be attributed to maltreatment. Also, in the 1980s, Fagan and his colleagues (1987) and Agnew (1985, 1989) found more evidence supporting the link between adolescent victimization and delinquency. In two empirical tests of his revision to strain theory, Agnew (1985, 1989), in attempting to see if environmental adversity had any effect on adolescent delinquency, included measures that allowed for the test of the effect of adolescent physical abuse on delinquency. Apart of his measure of environmental adversity was whether or not an adolescent had been kicked or slapped by a parent. In finding support for his claims that environmental adversity increases the likelihood of delinquent behavior, Agnew inadvertently found that adolescent abuse could possibly increase the chance of participating in later delinquent behavior. Fagan et al. (1987) examined inner-city adolescents to see if victimization played a role in participating in illegal behavior as adolescents. They found that of the males who had reported being victimized at least once during the past 12 months; almost 60% reported committing at least one delinquent act during the same time

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period. Of the females who had been victimized in the previous year, 45% reported they had participated in delinquency at least once in the previous year. More recent empirical studies echo the results of the earlier studies that adolescent maltreatment can lead to delinquent behavior. Vissing et al. (1991) found that adolescents who were victims of physical abuse committed more delinquency. This finding was similar to the results of DuRant et al.’s (1994) examination of African American adolescents. Also, Ireland and his colleagues (2002) and Thornberry and his colleagues (2001) used longitudinal data to examine the relationship between adolescent abuse and victimization and later delinquent behavior. Both groups of researchers found that victimization occurring only during the adolescent years resulted in more delinquency than maltreatment occurring only in childhood (age 11 and under) and a combination of childhood and adolescent victimization. Furthermore, Menard (2002) discovered that adolescent victimization (age 12 to 17) increased the odds of offending later in life. As literature on the effect of adolescent maltreatment is growing, it does suffer from some of the same problems that Doerner (1987) noted about research on the effect of childhood maltreatment on delinquency. Some of the studies, especially, those done in the 1980s, relied heavily on official data that limits the sample being used. Combining minor and serious offenses into a single measure of delinquency also masks any differential effects that victimization may have on different types of delinquent behavior and resilient factors. And finally, more needs to be done in looking at group differences in the relationship between adolescent victimization and delinquency. Another gap in the research literature on the effect of childhood victimization and literature on the effect of adolescent victimization is the lack of information on the effect of the source of the victimization. Green (1993) noted in his review of then current literature on childhood sexual abuse that most of the research does not examine the impact of the source of the victimization. Of the few articles have considered this, the results have been mixed. Finkelhor (1979) found no difference in the impact of sexual abuse by family members versus sexual abuse by nonfamily members. However, later Harrison and her colleagues (1989) showed that those abused by family members were more likely to participate in stimulant use than those who were abused by nonfamily members. Due to the lack of consensus in research on the


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effect of the type of perpetrator of the victimization, future research is needed to resolve this. Even though the present study is unable to test the effect of the source of the victimization on delinquency, this gap in the literature must be recognized.

VIOLENT OFFENDING The type of delinquency that has perhaps received the most attention by victimization researchers is violent offending. The research literature contains a large collection of articles that examine the effect of victimization on violent offending. A significant portion of this literature refers to the “cycle of violence” idea, the notion that being a victim of violence as a youth will lead to violent behavior later in life. For several decades, several articles on the effect of victimization and violent offending have found that victimized youth are more likely to be involved in violent behavior compared to nonvictimized youth (Alfaro 1981; Kruttschnitt and Dornfeld 1991; Nofziger and Kurtz 2005;Smith and Thornberry 1995; Widom 1989b). For example, Lewis et al. (1979) studied incarcerated boys and found that those who had committed more violent offenses were more likely to have experienced or witnessed extreme physical abuse than nonviolent boys. Also, Monane et al. (1984) examined a sample of youths receiving psychiatric services at a hospital. Among those who reported that they had been abused, 72% had been involved in extremely violent behavior as compared with 42% of the nonabused youths. Also, Rivera and Widom (1990) found using official data and a longitudinal design that victimization in childhood did increase the overall risk for violent offending, particularly in males and African Americans. Smith and Thornberry (1995) used longitudinal data and studied official and self-report data regarding various types of delinquency and records of a child protection agency on a sample of youth in New York. They found that a history of victimization did increase the probability for subsequent violent offending, similar to the findings of earlier research. As with the research on general offending, most of this research on the effect of victimization on violent offending deals with victimization that occurs during the childhood years. However, there is research growing regarding the effect of adolescent victimization on violent

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offending. In Paperny and Deisher’s (1983) review of the then current literature on the effect of maltreatment as an adolescent on subsequent violent offenders revealed several conclusions. One, adequate studies of the exposure to violence during adolescence were lacking. Two, retrospective research that did exist on male adolescent offenders revealed that a significant portion had suffered some sort of maltreatment during the adolescent years. Three, based on available research, an association between adolescent victimization and violent crime does exist. However enlightening Paperny and Deisher’s review may have been, it did suffer from several drawbacks. First, the emphasis on adolescent males leaves out a great deal of research that could explain why victimized female adolescents may commit violent offenses. Also, the authors did not point out if the research on adolescent maltreatment and violent offending considered racial differences at all. Furthermore, Paperny and Deisher (1983) did not go into detail about why the literature on adolescent victimization and violent offending is lacking. A few years later, Fagan and his colleagues (1987) conducted a study of inner-city youths to determine the relationship between victimization and delinquency. They found that while victimization appeared to be a significant factor in relation to the severity of general delinquency, victimization did not seem to explain much variation in violent offending. There could be several explanations for this finding. One explanation could be that the respondents were less willing to report that they had participated in violent offending due to giving socially acceptable responses. More recent research has also examined young, inner-city minorities. DuRant and his colleagues (1994) analyzed self-report data from a sample of urban African American adolescent males, found that among several predictors, personal victimization, including being a victim of a shooting, were associated with reported use of violence. Other more resent research used a broader sample in studying the adolescent maltreatment/violent offending link. For example, Menard (2002) examined a national probability household sample of adolescents. He found that being victimized as an adolescent greatly increased the probability of committing a violent offense later in life. Others have found similar results. Cleary (2000) studied high school students and found that violent behavior was more frequent among those who had been victimized compared with the nonvictimized


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students. Also, Ireland and his associates (2002) conducted a prospective study and found that being maltreated only during the adolescent years does increase the likelihood that one will participate in violent delinquency. More recently, Nofziger and Kurtz (2005), like Menard, used a national probability sample of adolescents and found that victimization significantly increased the probability of participating in violent offending. As beneficial as the more recent work on the effect of adolescent victimization on violent crime is to the research literature, it still has shortcomings. One is that much of the recent research does not mention the limitations of the data that were used, (Benda and Corwyn 2002; Cleary 2000; Menard 2002; Shaffer and Ruback 2002). As noted earlier, using official data may introduce bias in the sample in that most official data involves information from mostly the poor and minority populations. One way to alleviate this problem may be to use the method used by Ireland et al. (2002). They assess the effect of adolescent victimization on both self-reported and officially recorded delinquent behavior. Even though the present study is unable to incorporate the use of official statistics, using both may help to bring more insight into the adolescent maltreatment/violent offending relationship. Another problem with the later studies on the effect of adolescent victimization on violent crime is the lack of work done on racial and gender differences. It is wise when studying factors affecting violent crime to determine whether there are differential effects of these factors by race and gender. Menard (2002) and Ireland et al. (2002) both used race and gender as control variables without looking to see if there exists any racial or gender differences in the relationship between adolescent victimization and violent offending. The use of race and gender as control variables is insufficient and masks any racial or gender differences in the victimization/violent offending relationship. In sum, however, the literature on the effect of adolescent maltreatment on violent offending is small but growing and despite its shortcomings, adds to the research literature.

DRUG USE Literature on the effect of victimization on drug use has been steadily growing over the past few decades beginning with the 1987 work of

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Dembo and his colleagues on adolescents in a Florida juvenile detention center. Overall, the studies in this area appear to show a connection between victimization and subsequent use of drugs as adolescents and adults (Cavaiola and Schiff 1988; Dembo et al. 1987; Dembo et al. 1989; Dembo et al. 2000; Edwall et al. 1989; Harrison et al. 1989; Harrison et al. 1997; Kilpatrick et al. 2000; Perez 2000; Van Hasselt et al. 1992). However, as with the studies of victimization and other types of delinquency, this research suffers from a variety of problems that makes drawing conclusions from these studies problematic. Arellano (1996) reviewed the current literature on victimization and substance use and abuse and found several methodological weaknesses. One problem she noted was that several studies used legally involved or clinical samples such as those in drug treatment facilities (Dembo et al. 1989; Dembo et al. 2000; Edwall et al. 1989; Harrison et al. 1989; Van Hasselt et al. 1992). The results from such studies can cause an overestimation of the magnitude of the association between substance use and maltreatment. These populations are also more likely to have been exposed to turbulent environments growing up, and as such according to Arellano, it may be difficult to determine whether the substance use is the result solely of maltreatment. Another issue raised in Arellano’s review was the racial and gender composition of the samples used in these studies. A significant proportion of the research used samples that were exclusively of one gender and of one race (Dembo et al. 1989; Edwall et al. 1989; Harrison et al. 1989; Van Hasselt et al. 1992). This precludes these studies from making reliable tests across gender and racial groups. Another problem of using institutionalized samples and samples whose members are of one race or gender is the issue of generalizability. The results of studies using these samples can only be generalized to similar populations, not to the general population. Another problem in this literature is the problem of measurement. Most studies did not consider whether other variables such as conforming values played a role in an abused individual turning to drug use (Dembo et al. 1987; Dembo et al. 2000; Edwall et al. 1989; Harrison et al. 1989; Harrison et al. 1997; Van Hasselt et al. 1992; Widom et al. 1999). Of those that did include moderating and mediating variables, most found that these variables did have effects on the victimization/drug use relationship. For example, Dembo et al.


31

(1989) found that physical and sexual victimization had an indirect effect on illicit drug use through self-derogation. Also, Kilpatrick et al. (2000) found that familial drug problems mediated this relationship as well. And finally, as with most of the literature examining the consequences of victimization, this literature primarily relies on retrospective and cross-sectional data as well (Harrison et al. 1997; Kilpatrick et al. 2000; Perez 2000). This type of information is limited in several ways. One is that cross-sectional data are only taken at one place in time. It is harder to establish whether substance use or maltreatment came first. Prospective, longitudinal data are much better at establishing causal order for maltreatment and substance use. And as it has been stated earlier, in regards to the victimization and offending literature, retrospective and prospective longitudinal data sometimes reveal different findings. For example, Widom et al. (1999) performed both retrospective and prospective studies on the same sample and found that prospectively, victims were not at an increased risk for drug abuse. However, retrospective results showed that victimization was associated with increased risk for drug use. This may be due to the severity or age of onset of the abuse experiences, which Widom and her colleagues did not control for. It could be that in the retrospective study, the respondents remembered only the most severe abuse, which probably occurred with less frequency. And prospectively, victimization with various levels of severity could have been collected which may have decreased its relationship with the risk of drug use. Yet another weakness of the literature that Arellano (1996) noted was its lack of theoretical grounding. Most studies do not base their analyses on any theory (Dembo et al. 1987; Dembo et al. 1989; Dembo et al. 2000; Edwall et al. 1989; Harrison et al. 1989; Harrison et al. 1997; Kilpatrick et al. 2000; Van Hasselt et al. 1992). Arellano (1996) contended that because of the atheoretical nature of the literature on victimization and drug use, it is descriptive at best. It also makes it impossible to define which mechanisms connect victimization and drug use. By employing the use of Agnew’s general strain theory, the present study addresses this gap in the literature on the victimization/drug use relationship. There are several problems with the research on the effect of victimization on drug use that Arellano (1996) did not consider. One is that most studies on the effect of maltreatment on substance use do not

32


distinguish between childhood and adolescent victimization (Dembo et al. 1987; Dembo et al. 1989; Dembo et al. 2000; Edwall et al. 1989; Harrison et al. 1989; Harrison et al. 1997; Kilpatrick et al. 2000; Perez 2000; Van Hasselt et al. 1992). A significant portion of the research operationalizes physical maltreatment by asking participants if they had ever been hit or beaten by someone so hard that it left marks and sexual maltreatment by asking if anyone had been sexual with them. An affirmative response from an adolescent or an adult to either could refer to victimization during childhood or victimization during the adolescent years. Considering that Thornberry and his colleagues (2001) and Ireland and his colleagues (2002) found that victimization during various stages of one’s youth could have different effects on the likelihood of proceeding into delinquent behavior, one might assume that the effect of childhood victimization on subsequent drug use may be different from the effect of adolescent victimization on drug use. Therefore, it is important to isolate when the maltreatment occurred. That is why the present study focuses specifically on the effect of adolescent victimization. Another gap in the research literature is the lack of studies that consider the effect of victimization on polydrug use or multiple drug use. Only a few studies have addressed whether victimization has any effect on multiple drug use. Harrison and her colleagues (1997) examined students in Minnesota and found that use of multiple substances was highly elevated among victims of maltreatment with the highest rates seen among individuals who reported being victims of both physical and sexual abuse. Their study did not separate childhood from adolescent victimization nor was it grounded in theory. Also, they did not look at race or gender differences in the victimization/multiple drug use relationship. In Menard’s (2002) study of the consequences of adolescent victimization, it was found that being the victim of property crime as an adolescent was more closely related to polydrug use later in life. He, like Harrison et al. (1997), did not look at racial or gender differences nor was his research grounded in any theoretical perspective. In a related study, Locke and Newcomb (2004) tested whether problems associated with polydrug use mediated the effect of childhood maltreatment on poor parenting practices such as not paying attention to one’s children. It was found that childhood maltreatment did lead to problems associated with polydrug use. Even though they did not


33

actually measure polydrug use, their results do lend some evidence to the idea that victimization during childhood can lead to polydrug use later in life. However, like Menard (2002) and Harrison et al. (1997) they did not test racial differences.

NONVIOLENT, NON-DRUG OFFENDING There has not been as much attention given to the effect of victimization on nonviolent, non-drug offending compared to the amount of work on the effect of victimization on violent offending and illicit drug use. This is primarily due to researchers focusing on predicting illicit drug use, violent offending or general delinquency, where many types of offending are collapsed into a single measure (Brezina 1998; Ireland et al. 2002; Lemmon 1999; McCord 1983; Vissing et al. 1991; Zingraff et al. 1994). In focusing on these forms of delinquency, it is impossible to determine how victimization affects nonviolent, non-drug offending. In literature that does examine this outcome, results are somewhat mixed. Malinosky-Rummell and Hansen (1993) concluded in their literature review that the majority of the research literature has not found a relationship between physical abuse and nonviolent, non-drug criminal behavior. However, they did include work (e.g. Pollock et al. 1990) that suggested that physical abuse did have an effect. They noted that since this work suffered from methodological problems such as not controlling for family variables, its results could not be trusted. More recent work by Widom and White (1997), however, has found that victimized children were more likely to be arrested for nonviolent, non-drug offenses than those in a control group. Also, Herrera and McCloskey (2003) found using longitudinal data that childhood sexual abuse of females was significant in predicting nonviolent delinquency. The difference in findings from the earlier to later studies, the subsequent work makes one believe that there actually may be a connection between victimization and nonviolent, non-drug delinquency. Some researchers have looked at the effect of victimization on specific types of nonviolent, non-drug offenses such as running away, prostitution and property offending. Probably the most well-known nonviolent, non-drug offense that has been studied by those looking at the effects of victimization is prostitution. There is a significant amount

34


of research that ties victimization as a youth to prostitution later in life. For example, Browne and Finkelhor’s (1986) literature review examining the effects of childhood sexual abuse described several studies that concluded that being sexually abused as a child increased the likelihood of engaging in prostitution later in life. Chesney-Lind's (1997) widely cited research of delinquent girls also seemed to suggest that girls involved in delinquency, including prostitution, seemed to have been abused previously. The idea that victimization (especially in girls) leads to an increase in the probability of running away from home is very established in the literature. Chesney-Lind’s (1997) research indicates that victimization is what drives many girls to run away from home. She concludes that running away was the only way to escape maltreatment. In contrast, Herrera and McCloskey (2003) found that childhood victimization was not significant in predicting whether or not a girl would run away. Furthermore, Kaufman and Widom’s (1999) examination of data from a prospective cohort design study where victimized children were matched with a control group. They discovered that children who were victimized were found to be more likely to run away from home. Even thought the present study is unable to include prostitution or running away in its analyses, it is still important to acknowledge the presence of this information in the literature. Along with prostitution and running away, researchers have also considered the effect of victimization on property offending. Earlier articles examining this relationship has not been successful in finding a relationship. For example, McCord’s 1979 prospective study found that parental aggression (including physical abuse) did not contribute significantly to the variance in property crime. Similarly, Fagan et al.’s (1987) study discovered that victimization explained very little variance in property offending. Also, an examination of data from a prospective cohorts design study by Zingraff and his colleagues (1993) found that maltreated children did not significantly differ from a comparison group of school children in terms of property offending. Even though these studies seem to answer the question about whether victimization affects property offending, each has methodological problems that may affect their results. McCord’s (1979) definition of parental aggression included a variety of behaviors such as throwing things, physical abuse, as well as shouting but did not include sexual abuse. By not separating physical abuse and testing to


35

see if it alone related to property offending, it is hard to determine from her study if the physical abuse aspect of her measure of parental aggression was related to property offending. Furthermore, the study by Fagan and his colleagues (1987) measured victimization by including whether the respondents had been victimized or if anyone the respondents knew had suffered victimization. Finding no variation in property offending could be due to the victimization of the people that the respondents know overshadowing the effect of the victimization of the respondents themselves. If knowing that others had been victimized had no effect on property offending and it was combined with respondents’ victimization, this would surely confound the relationship between experienced victimization and property offending. In addition, Zingraff et al.’s (1993) study has been criticized by Heck and Walsh (2000) who noted that Zingraff and his colleagues only looked at information on youth up to age 15. Also, the 1993 study measured delinquency as a dichotomous variable and did not consider the frequency of offending. Heck and Walsh conducted a study using the same data as used in the Zingraff et al. (1993) study, corrected the aforementioned methodological flaws and found that maltreatment was, in fact, important in predicting property crime. Just as Heck and Walsh (2000) were able to find a relationship between victimization and property offending other more recent work has shown a relationship as well. Stouthamer-Loeber et al. (2001), using data from longitudinal study of a sample of youths, found that individuals who reported having experienced victimization had also been referred to court for property offenses. Similarly, Perez (2001) discovered in a cross-sectional study of Mexican Americans and nonHispanic Whites that a history of physical or sexual victimization significantly increased the likelihood of involvement in property offending. Furthermore, Menard’s (2002) study of the consequences of adolescent victimization using longitudinal data found that the odds of participating in property offending later in life are nearly tripled by being a victim of violent crime as an adolescent. Even though the more recent articles predicting property offending do add to the research literature regarding the effects of victimization and are more methodologically sound (e.g. better definitions of victimization), they suffer from some of the same flaws as mentioned earlier. For example, the lack of information on racial and gender differences in articles on the effect of victimization and nonviolent,

36


non-drug offending leaves yet another gap that the present study attempts to fill. Furthermore, as with most literature on the effect of victimization on delinquency, most of these studies predicting nonviolent, non-drug offending deal with the effect of childhood victimization. Of those few articles that consider the effect of adolescent victimization (Menard 2002), most find that victimization in adolescents does increase the likelihood of at least some forms of nonviolent, non-drug offending.

RACE AND GENDER DIFFERENCES As alluded to earlier in this text, a significant portion of studies on the effect of victimization on various forms of delinquency did not examine group differences in the victimization/delinquency relationship. Several arguments show that it is important to consider racial and gender differences when looking at the effect of victimization on delinquency. Recall that Broidy and Agnew (1997) argued that males and females experience different kinds of strain that causes gender specific responses. They noted that males are more often exposed to financial strain and interpersonal conflict that might lead to property crime and violent offending. Females, they stated, usually experience strain that involves restriction of criminal opportunities and may lead to more self-destructive forms of behavior such as drug use. This is somewhat of a contrast to what has been argued earlier regarding gender differences in delinquency. Horowitz and White (1987) found that conforming to gender role expectations was associated with gender-specific styles of pathological behavior. For females, conforming to expectations of femininity led to psychological distress such as depression rather than delinquent behavior. For males, conforming to the stereotypical masculine role was found to lead to more delinquent behavior. As far as racial differences are concerned, Eitle and Turner (2003) argued that African Americans are exposed to more stress (or strain) than Whites that would lead to more criminal behavior by African Americans. Since victimization can be seen as a source of strain or stress, it follows that Broidy and Agnew’s ideas about gender and Eitle and Turner’s notions about race can be applied to the victimization/ delinquency relationship. Thus, there exists conceptual justification for


37

looking at racial and gender differences in the effect of victimization on delinquent behavior. There are several reasons that some studies on the effect of maltreatment on various forms of delinquency do not examine racial or gender differences. One reason is a lack of diversity in the samples (Edwall et al. 1989; Heck and Walsh 2000; Lemmon 1999; McCord 1983; Van Hasselt et al. 1992). Samples in these studies are made up mostly of one particular race or gender. This could cause limited generalizability of the results. Another explanation is that some researchers simply decide that using gender and race as control variables is enough (Agnew 2002; Agnew and White 1992; Ireland et al. 2002; Kaufman and Widom 1999; Kilpatrick et al. 2000; Kruttschnitt and Dornfeld 1993; Zingraff et al. 1993; Zingraff et al. 1994). Some work has attempted to look at gender and racial differences in the effect of victimization on various types of delinquency. In articles on the effect of victimization on violent offending, it is evident that there is a gender difference. However, the exact nature of that difference is unclear. Widom’s (1989a) well-cited study found that victimization led to violent crime in males but was not significant in predicting violent crime in females. Another study by Widom (1989c) confirmed these findings. However, some later studies produced different results. Rivera and Widom (1990), Maxfield and Widom (1996), Widom and White (1997) found that victimized females were at an increased risk for violent arrests, whereas victimization was not significant at all for male violent crime. This finding was made across different types of data. Rivera and Widom’s (1990) study was based on a cross-sectional sample of violent offenders. However, Maxfield and Widom (1996) and Widom and White (1997) used a prospective study. More recently, however, Cleary (2000) found that victimized males were at a greater risk of violent behavior than victimized females. He looked at the effect of adolescent victimization on violent behavior while Rivera and Widom (1990), Maxfield and Widom (1996), and Widom and White (1997) all examined the effect of childhood victimization on violent offending. As far as racial differences in the victimization/violent offending relationship are concerned, there is more consistency in the findings in the literature. Widom (1989a) found that for African Americans, victimization increased the likelihood of having a violent arrest but did

38


not increase that risk for Whites. A year later, Rivera and Widom (1990) found that victimization did not significantly increase the risk of violent arrests in Whites. However, it did significantly increase the risk of violent arrests in African Americans. Kruttschnitt and Dornfeld (1991) and Maxfield and Widom (1996) discovered similar results. The literature seems to show that victimization increases the risk of violent offending in African Americans but not for Whites. However, all of these studies consider the effect of childhood not adolescent victimization. In looking at the literature on the effect of victimization on drug use, there has been very little done in terms of racial differences. Agnew and White (1992), Dembo et al. (1987), and Dembo et al. (1989) controlled for race in their models and found that race only affected the amount of drugs used. Each of these studies found that Whites were using more drugs than African Americans. They did not attempt to test their models across racial groups. However, Harrell (2001), using cross-sectional data of youth aged 11 to 17, found that the effect of victimization increased the likelihood of drug use in Whites but not in African Americans. In looking at gender differences in the victimization/drug use relationship, a few researchers have compared models for males and females. However, like research on the effect of victimization on violent offending, the exact nature of the gender difference has not been clearly established in the literature. Harrison et al. (1989) found that victimized males used inhalants more than nonvictimized males. However, there was no difference for females. Also, Dembo et al. (2000) did find that their model of abuse predicting drug use fit better for males than females. Conversely, Widom et al. (1999) found that maltreated females were more likely than maltreated males to meet the DSM-III-R criteria for abuse/dependence of cocaine and stimulants. Also, Widom and White (1997) found that victimized females were at an increased risk for drug abuse where victimized males were not. In the research that has looked at the effect of maltreatment on nonviolent, non-drug offending, there has been very little done on gender differences. Widom (1989c) found that for both males and females, victimization was a significant factor in predicting the frequency of property offending. Victimization was significant for females but not for males in predicting public order crime. Widom and


39

White (1997) found that victimization increased the risk for arrests for nonviolent crime in both males and females, with no gender difference. However, neither study looked at the effect of adolescent victimization on nonviolent, non-drug offending. To the knowledge of this researcher, no one has studied racial differences in terms of the effect of victimization on nonviolent, non-drug offending.2 Despite its somewhat equivocal nature, the conceptual and empirical information presented in this section provides evidence to support specific hypotheses regarding racial and gender differences in the effect of victimization on violent offending, illicit drug use, and nonviolent, non-drug offending. While the differential effects of victimization on all offending outcomes will be explored by race and gender, several specific hypotheses were tested as follows:

HYPOTHESES 1.

2. 3. 4.

5.

6.

7.

Adolescent victimization, an indicator of negative stimuli in general strain theory, will be positively related to violent offending. Adolescent victimization will be positively related to illicit drug use. Adolescent victimization will be positively related to nonviolent, non-drug offending. Due to gender differences in reactions to strain, adolescent victimization will have a greater positive effect in predicting illicit drug use in females than in males. Due to gender differences in reactions to strain, adolescent victimization will have a greater positive effect in predicting violent offending in males than in females. Due to racial differences in reactions to strain, adolescent victimization will have a greater positive effect in predicting violent offending by African Americans compared to Whites. Due to racial differences in reactions to strain, adolescent victimization will have a greater positive effect in predicting illicit drug use by Whites compared to African Americans.


CHAPTER 4

The National Youth Survey and Growth Curve Modeling

DATA The data used in the present study came from the National Youth Survey (NYS). This currently, on-going longitudinal study that was conducted by Delbert Elliott beginning in 1976 is very well known in the field of criminology.3 The goal of the survey is to examine changing attitudes, beliefs and behaviors about topics such as career goals, involvement with community and family, attitudes about violence, drugs, and social values in a portion of the American population (Elliott et al. 1988). The data for the NYS are collected by the Institute for Behavioral Science of the University of Colorado in Boulder.4 The National Youth Survey has been sponsored by several government agencies over the life of the study. At the start of the survey, funding came primarily from the National Institute of Mental Health with supplemental funding from the Office of Juvenile Justice and Delinquency Prevention and the National Institute of Justice (Elliott et al. 1988; Elliott 1994).5 Researchers for the NYS began by selecting a probability sample of 8,000 households in the United States. All youths living in the selected households who were 11 through 17 years of age on December 31, 1976 and were physically and mentally capable of being interviewed were eligible respondents for the study. From these households, a sample of 2,360 adolescents, aged 11 to 17, was selected to be part of the National Youth Survey. In 1976, each of these 2,360 individuals was asked to be interviewed. Of these individuals, 1,725 (73%) agreed to participate in the NYS (Elliott et al. 1985, Elliott et al. 1989).6 41

42


In 1977, the first wave of the study was conducted and each of the 1,725 adolescents participated in confidential interviews to obtain information regarding behavior they had participated in during the previous year. The sample respondents were asked questions on a variety of topics such as alcohol and drug use and participation in other delinquent behavior. The adolescents were also asked questions about other aspects of their lives such as their aspirations, relationships with family members and peers and their attitudes towards delinquent behavior. Also, during this initial interview, a parent or legal guardian of each adolescent in the sample was interviewed. These adults were asked about a variety of topics including their careers, demographics and social values (Elliott et al. 1989). After the initial wave, the adolescents were interviewed at subsequent times in 1978, 1979, 1980, 1981, 1984, 1987, 1990, and 1993. During these waves, like the first wave, those in the sample were asked about behavior in the previous year. Therefore, the information collected during these years reflected behavior for 1977-1980, 1983, 1986, 1989 and 1992 (Menard 2002). The present study used a portion of the NYS sample. From the original 1,725 respondents, only those who were White or African American were selected for the present study. This was because there were not enough respondents who were of other ethnicities (such as Hispanics, Asians) to conduct race specific analyses. Using only Whites and African Americans reduced the number of respondents to 1,621. This number was further reduced by selecting from those 1,621 adolescents only those who reported that they were from 11 to 15 years old in 1976. This further reduced the panel size to 1,203 individuals. It is these 1,203 respondents that make up the panel used in the present study. Of these 1,203 individuals, 200 were African American, 1,003 were White. Also, the sample of 1,203 consisted of 633 males and 570 females. This study also used the first four waves of the National Youth Survey. With the first four waves, the age of the respondents covered by the present study was as follows: For those Whites and African Americans who were 11 years old in 1976, the ages covered in the current study are 11, 12, 13, and 14. For those Whites and African Americans who reported that they were 12 in 1976, this study covered information when they were ages 12, 13, 14, and 15. For those Whites and African Americans who reported that they were 13 in 1976, the


43

current study covered information reported when they were ages 13, 14, 15, and 16. Also, for those Whites and African Americans who reported that they were 14 years old in 1976, the present study covered information reported when they were ages 14, 15, 16, and 17. For those Whites and African Americans who reported that they were 15 years old, the present study covered information reported when they were ages 15, 16, 17, and 18. Therefore, the present study will cover information on victimization and delinquency that occurs during from as young as age 11 up to and including age 18. Even though, as stated in the previous chapter, there is ambiguity in the research literature as to what ages actually constitute adolescence, there is some precedent for assuming that the ages of the individuals in the panel used in the present study are adolescent years. Fagan (2003) and Menard (2002) both used the NYS and used victimization information from the NYS respondents when the respondents were between and including the ages of 11 and 17 in 1976. Both authors referred to victimization during these years as adolescent victimization. Also, other researchers not using the National Youth Survey have operationalized adolescent victimization as occurring during those years as well (Shaffer and Rubach 2002). The current study also included victimization at age 18 as adolescent victimization, which is consistent with other researcher as well who did not use the NYS (Caviola and Schiff 1988). The National Youth Survey is a good data set from which to test the efficacy of GST for several reasons. First, it is a longitudinal study that measures offending behavior over several years. This will allow the effects of victimization to be examined over time. Another advantage of using the NYS is that these data come from a national probability household sample instead of a legally involved or clinical sample like other studies mentioned earlier. Results based on these data can be generalized to a broader population, not just other clinical or otherwise institutionalized populations. More importantly, several studies have shown that the NYS does provide good measures for general strain theory. Paternoster and Mazerolle (1994), Mazerolle and Maahs (2002), and Mazerolle (1998) all have used the first two waves of the National Youth Survey to test general strain theory. They noted that these data provided very good measures with which to test the theory.

44


DATA LIMITATIONS Even though the National Youth Survey has an opportunity to obtain information that may not be gathered in official statistics, it does have its limitations. One weakness is attrition that increased slowly during the years of the NYS. Of the original full NYS sample of 1,725 respondents, over 94 percent participated in the survey at wave two. The rate of retention for the full NYS sample was 96 percent for wave three and 94 percent for the 1979 wave (Elliott et al. 1985; Menard 2002). More information regarding the effects of missing data as a result of attrition and other reasons on the present study is discussed in the Appendix. A second limitation of the NYS data is that there may be possible recall issues with the respondents. Respondents may forget, under-report, or exaggerate answers. They may also modify their responses to give socially acceptable answers.

DEPENDENT VARIABLES Violent Offending There are three dependent variables used in the analyses to represent Agnew’s (1992) idea of delinquent responses to strain. One is a measure of violent offending. It is a variable that is a composite of several questions that were asked during the first four waves of the National Youth Survey. During these waves of the NYS, the youths were asked the following: 1. “How many times in the last year have you attacked someone with the idea of seriously hurting or killing him/her?” 2. “How many times in the last year have you been involved in gang fights?” 3. “How many times in the last year have you hit (or threatened to hit) a teacher or other adult at school?” 4. “How many times in the last year have you hit (or threatened to hit) one of your parents?” 5. “How many times in the last year have you had (or tried to have) sexual relations with someone against their will?”

The National Youth Survey and Growth Curve Modeling 6.

7.

8.

“How many times in the last year have you used (strong-arm methods) to get money or things from students? “How many times in the last year have you used (strong-arm methods) to get money or things teachers?” “How many times in the last year have you used (strong-arm methods) to get money or things from people (not teachers or students)?”

45 force other force from force other

For each of the first four waves, answers to the questions regarding violent behavior were added for each adolescent to create a variable that represents the number of violent offenses that an adolescent committed during each wave. This variable was called VIOLENT1 for wave one, VIOLENT2 for wave two, etc. When the means and standard deviations of the four violent offending variables were taken for those in the full panel who did not have missing data on the violent offending variables, large standard deviations were found due to extreme outliers in the data. Extreme outliers were those values that were found to be over two standard deviations above the mean. There was concern about the influence that these outliers would have on the analyses. The first action taken to reduce the influence of the outliers was to take the square root of all of the values on the variable. However, after doing so, the distribution of the transformed values was very similar to the distribution of the original values. Other transformations typically used with skewed data such as a logarithmic transformation or taking the inverse of all of the values on the variable (Tabachnick and Fiddell 2003) were not used in this case. This was because there were a significant number of individuals who reported no violent offending and taking the inverse of zero or obtaining a base 10 or natural logarithm of zero does not produce a value that can be used in multivariate analyses. Adding a value of one, then obtaining either a base 10 or natural logarithm only produced a distribution that was very similar to that of the original variables. Adding a value of one and then taking the inverse produced a very negatively skewed distribution. Therefore, it was decided that the values of the outliers on the variables representing violent offending at the first four waves of the NYS should be changed. To change the values of the outliers, the

46


distribution of these four variables measuring violent offending were obtained. Those values that were over two standard deviations above the mean were changed to equal a value that was one unit higher than the highest value in the distribution that was not two standard deviations above the mean. Changing the value of outliers as a way of reducing their influence has been mentioned by Tabachnick and Fiddell (2003) as one of the possible ways of dealing with outliers. After the outliers of the four violent offending variables were transformed for the entire panel of 1,203 individuals, means and standard deviations were obtained for the entire sample and each race and gender group. The data were also examined for missing data; out of the 1,203 individuals in the panel used in the present study, 4 individuals had missing data on the violent offending variable at wave one, 44 individuals had missing data on the violent offending variable at wave two, 63 individuals had missing data on the violent offending variable at wave three, 122 individuals had missing data on the violent offending variable at wave four. Of the males in the panel for the present study, 2 individuals had missing data on the violent offending variable at wave one, 25 individuals had missing data on the violent offending variable at wave two, 33 individuals had missing data on the violent offending variable at wave three, 76 individuals had missing data on the violent offending variable at wave four. Of the females in the panel, 2 individuals had missing data on the violent offending variable at wave one, 19 individuals had missing data on the violent offending variable at wave two, 30 individuals had missing data on the violent offending variable at wave three, 46 individuals had missing data on the violent offending variable at wave four. For the African Americans in the panel, 1 individual had missing data on the violent offending variable at wave one, 12 individuals had missing data on the violent offending variable at wave two, 15 individuals had missing data on the violent offending variable at wave three, 19 individuals had missing data on the violent offending variable at wave four. For the Whites in the panel, 3 had missing data on the violent offending variable at wave one, 32 individuals had missing data on the violent offending variable at wave two, 48 individuals had missing data on the violent offending variable at wave three, 103 individuals had missing data on the violent offending variable at wave four.


47

Table 4.1 shows the descriptive statistics for the violent offending variables with the outliers changed. It does not include information from those aforementioned individuals with missing data on the violent offending variables. Examining the means over time, it can be seen that violent offending generally decreased over time. African Americans, on average, reported committing more violent offenses than Whites in the panel. Also, males reported more violent crime than females. One thing that is interesting to note is that female violent offending increased slightly at the fourth wave whereas, male violent offending decreased slightly between the third and fourth waves of the NYS. It is also interesting to see that for most of the racial and gender groups, the level of violent offending decreases over time. Table 4.1. Means, standard deviations, and variances for violent offending Total

Race Black

N Mean S.D. Var.

VIOLENT1 VIOLENT2 VIOLENT3 VIOLENT4 1199 1159 1140 1081 1.15 .93 .67 .63 4.16 3.68 2.00 2.26 17.30 13.57 4.02 5.13

n Mean S.D. Var. n Mean S.D. Var.

199 1.64 5.13 26.34 1000 1.06 3.93 15.47

188 1.04 3.58 12.84 971 .91 3.71 13.73

185 .91 2.07 4.29 955 .63 1.99 3.97

181 .66 2.64 7.00 900 .62 2.18 4.75

Gender Male n Mean S.D. Var. Female n Mean S.D. Var.

631 1.71 5.30 28.11 568 .54 2.14 4.59

608 1.43 4.81 23.13 551 .38 1.57 2.47

600 .90 2.44 5.94 540 .34 1.31 1.73

557 .89 2.49 6.19 524 .44 1.98 3.90

White

48


In the present study, adolescents of various ages were included in the panel used so the amounts of violent offending for a variety of ages were included in the means shown in the table. This probably contributed to the pattern of the means over time found during the first four waves of the NYS. This pattern of decreasing involvement in violent offending has been found by other researchers (Lauritsen 1998). An explanation for the decrease in violent offending over time in the sample could be due to attrition in the data. As shown in Table 4.1, the number of respondents in the panel for the present study who had sufficient data to create the violent offending variable decreased over time. Those who dropped out may have been among the most delinquent in the panel. Without their offending reported, the amount of violent offending in the panel could decrease. Nonviolent, Non-Drug Offending The second dependent variable used in the analyses measured nonviolent, non-drug offending. This variable, like the violent offending variable, was a composite of responses to several questions used during the first four waves of the NYS. These questions were: 1. “How many times in the past year have you purposely damaged destroyed property belonging to your parents or other family members?” 2. “How many times in the past year have you purposely damaged or destroyed property belonging to a school?” 3. “How many times in the past year have you purposely damaged or destroyed property that did not belong to you (not counting family or school property)?” 4. “How many times in the past year have you stolen (or tried to steal a motor vehicle, such as a car or motorcycle)?” 5. “How many times in the past year have you stolen (or tried to steal) things worth more than $50?” 6. “How many times in the past year have you broken into a building or vehicle (or tried to break in) to steal something or just to look around?” 7. “How many times in the past year have you knowingly bought, sold, or held stolen goods or tried to do any of these things?”


49

“How many times in the past year have you carried a hidden weapon other than a plain pocketknife?” An additive index was created that summed the answers to each of these questions and represents the number of nonviolent, non-drug offenses that an adolescent committed during each of the first four waves in the NYS. It was called NONVIO1 for wave one, NONVIO2 for wave two, etc. When the means and standard deviations of the four nonviolent, non-drug offending variables were taken for the full panel of 1,203 youths, large standard deviations were found due to extreme outliers in the data. The same method for reducing the influence of outliers that was used with the violent offending variables was used for the nonviolent, non-drug offending variables. As with the violent offending variables, there were missing data present on the variables representing nonviolent, non-drug offending. Among the entire panel of 1,203 individuals for the present study, 4 had missing data on the nonviolent, non-drug offending variable at wave one, 44 had missing data on the nonviolent, non-drug offending variable at wave two, 63 had missing data on the nonviolent, non-drug offending variable at wave three, 122 had missing data on the nonviolent, non-drug offending variable at wave four. Among the males in the panel, 2 had missing data on the nonviolent, non-drug offending variable at wave one, 25 had missing data on the nonviolent, non-drug offending variable at wave two, 33 had missing data on the nonviolent, non-drug offending variable at wave three, 76 had missing data on the nonviolent, non-drug offending variable at wave four. Among the females in the study, 2 had missing data on the nonviolent, non-drug offending variable at wave one, 19 had missing data on the nonviolent, non-drug offending variable at wave two, 30 had missing data on the nonviolent, non-drug offending variable at wave three, 46 had missing data on the nonviolent, non-drug offending variable at wave four. Among the African Americans in the panel used for the present study, 1 had missing data the on the nonviolent, non-drug offending variable at wave one, 12 had missing data the on nonviolent, non-drug offending variable at wave two, 15 had missing data on the nonviolent, non-drug offending variable at wave three, 19 had missing data on the nonviolent, non-drug offending variable at wave four. Among the Whites, 3 had missing data on the nonviolent, non-drug offending variable at wave one, 32 had missing data on the nonviolent, non-drug offending variable at wave two, 48 had missing data on the nonviolent,

50


non-drug offending variable at wave three, 103 had missing data on the nonviolent, non-drug offending variable at wave four. As in the case of the violent offending variable, the amount of those missing on the nonviolent/non-drug offending variable increased over time. This could also be due to attrition in the panel. Table 4.2 provides descriptive information about nonviolent and non-drug offending with the outliers changed. It does not include information from those aforementioned individuals with missing data on the nonviolent/non-drug offending variables. It shows that, overall, nonviolent, non-drug offending increased between the first and second waves and decreased thereafter for the panel. Whites followed this pattern. However, African Americans reported an increase in nonviolent, non-drug offending from the first to second waves, a decrease from the second to third waves and an increase from the third to fourth waves. Table 4.2. Means, standard deviations and variances for nonviolent, non-drug offending Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

NONVIO1 1199 2.58 7.00 49.00

NONVIO2 1159 2.61 12.18 148.39

NONVIO3 1140 2.26 8.17 66.78

NONVIO4 1081 2.20 8.84 78.09


199 2.63 7.53 56.63 1000 2.56 6.89 47.57

188 2.43 11.52 132.72 971 2.65 12.32 151.55

185 1.39 5.74 32.92 955 2.42 8.56 73.21

181 2.63 10.76 115.77 900 2.12 8.40 70.59


631 3.77 8.70 75.67 568 1.25 4.01 16.10

608 4.23 15.57 242.55 551 .83 6.22 38.68

600 3.46 10.43 108.88 540 .92 4.09 16.74

557 2.92 9.25 85.53 524 1.45 8.32 69.22


51

In looking at gender differences, for the four waves, males reported more nonviolent, non-drug offending than females. The pattern of rises and falls in offending for males followed the pattern of the entire panel and Whites. The offending pattern over time for females was found to be similar to that of African Americans. Illicit Drug Use The third dependent variable used in this study measures the use of illegal substances. Like the other two outcome variables, it is a composite variable that shows the frequency of illegal drug use that an adolescent used during a particular wave. At each of the first four waves of the National Youth Survey, respondents were asked the following questions: 1. “In the last year, how often have you used marijuana?” 2. “In the last year, how often have you used hallucinogens?” 3. “In the last year, how often have you used amphetamines?” 4. “In the last year, how often have you used barbiturates?” 5. “In the last year, how often have you used heroin?” 6. “In the last year, how often have you used cocaine?” The answer choices for each of these questions during all waves were, “Never,” “Once or twice a year,” “Once every 2-3 months,” “Once a month,” “Once every 2-3 weeks,” “Once a week,” “2-3 times a week,” “Once a day,” and “2-3 times a day.” The response options for these questions were recoded from 0 to 8, with zero reflecting the response of “Never” and 8 reflecting “2-3 times a day.” This sum represented the frequency of illegal drug use for an adolescent for each wave. This variable was called DRUGS1 for wave one, DRUGS2 for wave two, etc. As with the other dependent variables, there was concern about the effect of outliers on this variable. As stated previously, commonly used methods such as taking the inverse or obtaining a logarithmic transformation were not used due to the fact that the distribution contained values (e.g. zero) that could not be transformed into values that could be used in multivariate analyses or the methods produced distributions that were very similar to that of the original variable. Therefore, the same method suggested by Tabachnick and Fidell (2001) that was used

52


with the other dependent variables was used for the all variables representing illicit drug use. As with the other offending variables, there were missing data present on the variables representing illicit drug use. Among the entire panel of 1,203 individuals for the present study, no one had missing data on the illicit drug use variable at wave one, 47 had missing data on the illicit drug use variable at wave two, 67 had missing data on illicit drug use variable at wave three, 122 had missing data on illicit drug use variable at wave four. Among the males in the panel, no one had missing data on illicit drug use variable at wave one, 26 had missing data on illicit drug use variable at wave two, 36 had missing data on illicit drug use variable at wave three, 76 had missing data on illicit drug use variable at wave four. Among the females in the study, no one had missing data on illicit drug use variable at wave one, 21 had missing data on illicit drug use variable at wave two, 31 had missing data on illicit drug use variable at wave three, 46 had missing data on illicit drug use variable at wave four. Among the African Americans in the panel used for the present study, no one had missing data on illicit drug use variable at wave one, 13 had missing data on illicit drug use variable at wave two, 15 had missing data on illicit drug use variable at wave three, 19 had missing data on illicit drug use variable at wave four. Among the Whites, no one had missing data on illicit drug use variable at wave one, 34 had missing data on illicit drug use variable at wave two, 52 had missing data on illicit drug use variable at wave three, 103 had missing data on illicit drug use variable at wave four. Table 4.3 gives information about illicit drug use excluding the missing data. It shows that illicit drug use increased over the course of the first four waves of the NYS. The jump in the mean of illegal drug use from the second to third waves may be attributed to the significant increase in the reported use of marijuana, hallucinogens, and amphetamines. It shows that over time, Whites generally reported a higher use of drugs except at wave one. Females reported less use of illegal drugs during the first four waves of the NYS than males. This table shows that illegal drug use increases over time which is similar to results shown by other researchers (Jang 2002).


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Table 4.3. Means, standard deviations, and variances for illicit drug use Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

DRUGS1 1,203 .61 3.20 10.24

DRUGS2 1156 .61 1.83 3.37

DRUGS3 1136 1.39 2.49 8.91

DRUGS4 1081 1.40 3.04 9.24


200 .69 3.72 13.85 1003 .59 3.09 9.54

187 .48 1.50 2.25 969 .63 1.89 3.58

185 1.04 2.17 4.71 951 1.46 3.12 9.77

181 1.22 2.82 7.96 900 1.36 3.08 9.50


633 .74 3.33 11.06 570 .46 3.05 9.32

607 .75 2.08 4.33 549 .44 1.50 2.25

597 1.74 3.52 12.38 539 1.00 2.22 4.92

557 1.60 3.45 11.93 524 1.05 2.50 6.23

INDEPENDENT VARIABLES Presentation of Negative Stimuli A respondent’s previous victimization experiences were used as an indicator of Agnew’s (1992) concept of “negative stimuli.” The variables that were used in the analyses that represented victimization were created from a separate set of questions asked during the first four waves of the National Youth Survey. The victimization variable was a composite of the following questions asked during those four waves: 1. “In the last year, from the Christmas a year ago to the Christmas just past, has something been taken directly from you (or attempt to do so) by force or by threatening to hurt you?”

54

Adolescent Victimization and Delinquent Behavior 2.

“In the last year, from the Christmas a year ago to the Christmas just past, have you been beaten up by your mother or father?” 3. “In the last year, from the Christmas a year ago to the Christmas just past, have you been sexually assaulted or raped (or an attempt to do so)?” 4. “In the last year, from the Christmas a year ago to the Christmas just past, have you been attacked with a weapon such as a gun, knife, bottle, or chair by someone other than your mother or father?” 5. “In the last year, from the Christmas a year ago to the Christmas just past, have you been beaten up (or threatened with being beaten up) by someone other than your mother or father?” Respondents who answered yes to any of these questions were asked how many times they had suffered from that type of victimization in the past year. Respondents’ answers were added to create an index that represents the number of violent victimization events experienced for each respondent at each the first four NYS waves. If an adolescent stated that they had not experienced any of these types of victimizations for a particular wave, they were coded zero for that wave. This index was called VICT1 for victimization in wave one, VICT2 for victimization in wave two and so on. When the means and standard deviations of the victimization offending variables were taken for the full panel used in this study, large standard deviations were found due to extreme outliers in the data. The same method for reducing the influence of outliers that was used with the offending variables was used for the victimization variables. As with the offending variables, there were missing data present on the variables representing victimization. Among the entire panel of 1,203 individuals for the present study, 4 individuals had missing data on the victimization variable at wave one, 44 had missing data on the victimization variable at wave two, 63 had missing data on the victimization variable at wave three, 122 had missing data on the victimization variable at wave four. Among the males in the panel, 2 had missing data on the victimization variable at wave one, 25 had missing data on the victimization variable at wave two, 33 had missing data on the victimization variable at wave three, 76 had missing data on the victimization variable


55

at wave four. Among the females in the study, 2 had missing data on the victimization variable at wave one, 19 had missing data on the victimization variable at wave two, 30 had missing data on the victimization variable at wave three, 46 had missing data on the victimization variable at wave four. Among the African Americans in the panel used for the present study, 1 had missing data on the victimization variable at wave one, 12 had missing data on the victimization variable at wave two, 15 had missing data on the victimization variable at wave three, 19 had missing data on the victimization variable at wave four. Among the Whites, 3 had missing data on victimization variable at wave one, 32 had missing data on victimization variable at wave two, 48 had missing data on the victimization variable at wave three, 103 had missing data on the victimization variable at wave four. As with other previously mentioned variables, the amount of missing data on the victimization variable increases over time, this may be due to attrition in the panel used in this study. Table 4.4 gives descriptive information about the victimization variables with the outliers changed and missing data excluded. It shows that reported victimization decreased over the first four waves of the NYS for the full panel in this study. Whites, males, and females in the panel reported a similar decline in reported victimization, African Americans, however reported an increase in victimization at the fourth wave. Furthermore, males, on average, reported more victimization than females. The general decrease in reported victimization displayed in Table 4.4 has been found by other researchers (Lauritsen 1998).

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Table 4.4. Means, standard deviations, and variances for victimization Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

VICT1 1199 1.87 3.98 15.83

VICT2 1159 1.20 3.12 9.77

VICT3 1140 1.01 2.62 6.86

VICT4 1081 .86 2.21 4.90


199 2.06 4.15 17.25 1000 1.83 3.94 15.55

188 1.05 2.22 4.92 971 1.23 3.27 10.70

185 .69 1.78 3.18 955 1.08 2.75 7.56

181 1.04 2.63 6.91 900 .82 2.12 4.49


631 2.55 4.63 21.41 568 1.11 2.93 8.57

608 1.69 3.79 14.38 551 .66 2.03 4.12

600 1.39 2.99 8.94 540 .60 2.05 4.24

557 1.13 2.56 6.55 524 .57 1.73 2.99

Constraints to Illegal Coping As stated earlier, Agnew argued (1992) that the presence of strain alone, as measured by negative stimuli, does not cause criminal behavior to emerge. It is one’s response to that strain that may be illegal. He contended that the choice to respond to strain with illegal behavior might be constrained by several factors including individuals’ values towards delinquency, goals, means for social support, and association with delinquent peers. Accordingly, using questions from the first four waves of the National Youth Survey, variables representing each of these factors were created. To measure respondents’ values toward conforming behavior, the following questions regarding their attitudes towards conforming behavior were used:


57

“How wrong is it for someone your age to purposely damage or destroy property that does not belong to him or her?” 2. “How wrong is it for someone your age to use marijuana or hashish?” 3. “How wrong is it for someone your age to hit or threaten to hit someone for no reason?” 4. “How wrong is it for someone your age to break into a vehicle or building to steal something?” 5. “How wrong is it for someone your age to sell hard drugs such as cocaine, heroin or LSD?” 6. “How wrong is it for someone your age to steal something worth more than $50?” Each question had the following response choices: “Very Wrong,” “Wrong,” “A Little Bit Wrong,” “Not Wrong at All.”The responses were coded from 1 to 4 with 1 reflecting the response of “Not Wrong at All,” 2 reflecting the choice of “A Little Bit Wrong,” 3 representing “Wrong,” and 4 reflecting the choice of “Very Wrong.” At each of the four waves, the responses to these questions were summed. As such, compared to lower scores, higher scores reflect approving attitudes toward conforming behaviors. This additive index was called VALUES1 for wave one, VALUES2 for wave two and so on. Similar to the previously discussed variables, there were missing data present on the variables representing victimization. For the full panel used in the present study, 4 had missing data on the conforming values variable at wave one, 44 had missing data on the conforming values variable at wave two, 63 had missing data on the conforming values variable at wave three, 122 had missing data on the conforming values variable at wave four. Among the males in the panel, 2 had missing data on the conforming values variable at wave one, 25 had missing data on the conforming values variable at wave two, 33 had missing data on the conforming values variable at wave three, 76 had missing data on the conforming values variable at wave four. Among the females in the study, 2 had missing data on the conforming values variable at wave one, 19 had missing data on the conforming values variable at wave two, 30 had missing data on the conforming values variable at wave three, 46 had missing data on the conforming values variable at wave four.

58


Among the African Americans in the panel used for the present study, 1 had missing data on the conforming values variable at wave one, 12 had missing data on the conforming values variable at wave two, 15 had missing data on the conforming values variable at wave three, 19 had missing data on the conforming values variable at wave four. Among the Whites, 3 had missing data on conforming values variable at wave one, 32 had missing data on conforming values variable at wave two, 48 had missing data on the conforming values variable at wave three, 103 had missing data on the conforming values variable at wave four. As with previously mentioned variables, the number of individuals from the panel for the present study who had information on these variables decreased over time. This could be due to attrition in the NYS. Table 4.5 gives descriptive information about the conforming values variable excluding the missing data. For the panel of individuals used in this study, the table shows that attitudes towards deviant behavior only slightly moved towards disapproving conforming behavior during the first four waves of the NYS. Table 4.5 also shows that females had slightly approving attitudes towards conforming behavior than males. Furthermore, African Americans and Whites were very close in terms of their conforming values. To measure respondents’ belief in societal goals, another composite variable, made up of three items asked during the first four waves of the NYS was created. The questions used in creating the variable measuring one’s goals were: 1. “How important is it to you to have a good job/career after you’re finished with school?” 2. “How important is it to you to get married?” 3. “How important is it to you to have children of your own?” The answer choices for each of these questions were “Very important,” “Somewhat important,” and “Not important at all.” The response options were coded 1 to 3 with 1 representing “Not important at all,” 2 representing “Somewhat important,” and 3 representing “Very Important.” At each of the four waves, the responses to these questions were summed. At wave one, this variable was called GOALS1. At wave two, it was called GOALS2 and so on. Compared to lower values, higher values on the additive index indicate that a respondent felt that these societal goals were more important.


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Table 4.5. Means, standard deviations, and variances for conforming values Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

VALUES1 1199 21.95 2.36 5.58

VALUES2 1159 21.65 2.57 6.59

VALUES3 1140 21.08 2.77 7.68

VALUES4 1081 20.92 2.84 8.04


199 21.78 2.38 5.67 1000 21.99 2.36 5.56

188 21.58 2.43 5.88 971 21.66 2.59 6.73

185 21.55 2.50 6.24 955 20.99 2.81 7.92

181 21.23 2.50 6.27 900 20.89 2.90 8.38


631 21.58 2.61 6.81 568 22.36 1.98 3.92

608 21.16 2.85 8.13 551 21.19 2.08 4.34

600 20.58 2.96 8.74 540 21.64 2.44 5.93

557 20.42 2.95 8.69 524 21.46 2.61 6.81

Similar to the previously discussed variables, there were missing data present on the variables representing goals. For the full panel used in the present study, 4 individuals had missing data on the goals variable at wave one, 44 had missing data on the goals variable at wave two, 63 had missing data on the goals variable at wave three, 122 had missing data on the goals variable at wave four. Among the males in the panel, 2 had missing data on the goals variable at wave one, 25 had missing data on the goals variable at wave two, 33 had missing data on the goals variable at wave three, 76 had missing data on the goals variable at wave four. Among the females in the study, 2 had missing data on the goals variable at wave one, 19 had missing data on the goals variable at wave two, 30 had missing data on the goals variable at wave three, 46 had missing data on the goals variable at wave four. Among the African Americans in the panel used for the present study, 1 had missing data on the goals variable at wave one, 12 had missing data on the goals variable at wave two, 15 had missing data on

60


the goals variable at wave three, 19 had missing data on the goals variable at wave four. Among the Whites, 3 had missing data on goals variable at wave one, 32 had missing data on goals variable at wave two, 48 had missing data on the goals variable at wave three, 103 had missing data on the goals variable at wave four. At with previously mentioned variables, attrition could be a fact to the increasing missing data on the goals variable. Excluding these missing data, Table 4.6 provides descriptive information about the goals variables. It shows that for the entire panel used in this study the level of belief in societal goals did not change much during the first four waves of the NYS. Also, it shows that, on average, Whites, slightly more so than African Americans, felt that the societal goals measured in this study were important. Also, it illustrates that there is hardly any gender difference in the importance in societal goals. Table 4.6. Means, standard deviations and variances for goals Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

GOALS1 1199 7.37 1.31 1.73

GOALS2 1159 7.28 1.30 1.70

GOALS3 1140 7.31 1.27 1.61

GOALS4 1081 7.30 1.29 1.66


199 7.14 1.39 1.93 1000 7.41 1.29 1.68

188 6.98 1.33 1.77 971 7.34 1.29 1.66

185 7.03 1.26 1.59 955 7.37 1.26 1.59

181 7.02 1.25 1.56 900 7.35 1.29 1.67


631 7.37 1.33 1.77 568 7.37 1.30 1.68

608 7.27 1.32 1.75 551 7.29 1.28 1.65

600 7.24 1.28 1.63 540 7.39 1.25 1.57

557 7.25 1.27 1.62 524 7.39 1.30 1.70


61

The social support variable was a composite of responses to four statements asked during the first four waves of the National Youth Survey. These statements were: 1. “My family is willing to listen if I have a problem.” 2. “My friends are willing to listen if I have a problem.” 3. “I feel close to my friends.” 4. “I feel close to my family.” The responses to these statements were “Strongly Agree,” “Agree,” “Neither Agree nor Disagree,” “Disagree,” and “Strongly Disagree.” For the four statements, higher values showed more social support (1 = Strongly Disagree. 5= Strongly Agree). For each of the first four waves of the NYS, an additive index was created that summed the responses to these questions. At wave one, this index was called SUPPT1, at wave two, this index was called SUPPT2 and so on. Compared to lower scores, higher scores reflected more levels of social support. Similar to the previously discussed variables, there were missing data present on the variables representing social support. For the full panel used in the present study, 4 individuals had missing data on the social support variable at wave one, 44 had missing data on the social support variable at wave two, 63 had missing data on the social support variable at wave three, 122 had missing data on the social support variable at wave four. Among the males in the panel, 2 had missing data on the social support variable at wave one, 25 had missing data on the social support variable at wave two, 33 had missing data on the social support variable at wave three, 76 had missing data on the social support variable at wave four. Among the females in the study, 2 had missing data on the social support variable at wave one, 19 had missing data on the social support variable at wave two, 30 had missing data on the social support variable at wave three, 46 had missing data on the social support variable at wave four. Among the African Americans in the panel used for the present study, 1 had missing data on the social support variable at wave one, 12 had missing data on the social support variable at wave two, 15 had missing data on the social support variable at wave three, 19 had missing data on the social support variable at wave four. Among the Whites, 3 had missing data on social support variable at wave one, 32 had missing data on social support variable at wave two, 48 had

62


missing data on the social support variable at wave three, 103 had missing data on the social support variable at wave four. As with previously mentioned variables, the increasing amount of missing data on the social support variable may be due to attrition on the panel used in this study. Table 4.7 gives descriptive information about the social support variables after excluding the missing data. It shows that the level of social support of the entire panel in the present study only slightly decreased over the first four waves of the National Youth Survey. It also shows that the level of social support reported was slightly lower for African Americans than for Whites. The level of social support reported was higher for females than for males. Table 4.7. Means, standard deviations, and variances for social support Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

SUPPT1 1199 16.35 1.96 3.85

SUPPT2 1159 16.41 1.96 3.83

SUPPT3 1140 16.58 1.90 3.60

SUPPT4 1081 16.77 1.90 3.62


199 15.70 2.09 4.35 1000 16.48 1.91 3.65

188 15.73 2.10 4.40 971 16.54 1.90 3.62

185 16.12 1.98 3.90 955 16.67 1.87 3.50

181 16.41 1.91 3.64 900 16.84 1.90 3.59


631 16.14 1.98 3.92 568 16.58 1.95 3.68

608 16.24 1.77 3.12 551 16.59 2.13 4.55

600 16.35 1.75 3.07 540 16.84 2.02 4.08

557 16.50 1.74 3.04 524 17.05 2.02 4.10

The constraining variable measuring one’s association with delinquent peers was also a composite of responses to several questions asked during each of the first four waves of the NYS. At waves one and


63

two, the respondents were first asked to give the first names or initials of up to eight individuals who they considered to be their close friends. Next, they were asked the following questions about them: 1. “Think about the people you listed as your close friends. During the last year, how many of them purposely damaged or destroyed property that did not belong to them?” 2. “Think about the people you listed as your close friends. During the last year, how many of them used marijuana or hashish?” 3. “Think about the people you listed as your close friends. During the last year, how many of them hit or threatened to hit someone without any reason?” 4. “Think about the people you listed as your close friends. During the last year, how many of them have broken into a vehicle or building to steal something?” 5. “Think about the people you listed as your close friends. During the last year, how many of them sold hard drugs such as heroin, cocaine and LSD?” 6. “Think about the people you listed as your close friends. During the last year, how many of them stolen something worth more than $50?” The answer choices for these questions were “All of Them,” “Most of Them,” “Some of Them,” “Very Few of Them,” and “None of Them.” These answers were coded so that higher scores represented more association with delinquent peers (5=All of Them…1=None of Them). At both waves, the responses to these questions were summed. For wave one, this variable was called DPEER1. For wave two, it was called DPEER2. Those who did not report having friends at a particular wave did not answer these questions and were coded missing on the delinquent peers variable for that wave. For waves three and four, the adolescents were asked questions that were similar to the six questions whether or not they gave the initials of their closest friends. The only difference between the questions asked at waves one and two and the questions asked at waves three and four was that at waves three and four the phrase “Think about the people you listed as your close friends” was not apart of the

64


questions regarding delinquent peers. The answer choices for the questions were the same as with waves one and two. Also, the responses to the questions were summed into an additive index as in the first two waves. For wave three, this index was called DPEER3. For wave four, this index was called DPREER4. There were 149 cases with missing data on this variable at wave one for the entire panel. Of these, 96 were male and 53 were female. Also, out of these 149 individuals, 112 were White and 37 were African American. At wave two, 155 individuals from the panel had missing data on the delinquent peers variable. This consisted of 93 males and 62 females. This also consisted of 116 Whites and 39 African Americans. At wave three, there were 67 individuals who had missing data on the delinquent peers variable. This consisted of 32 females and 35 males. Also, this number consisted of 49 Whites and 18 African Americans. At wave four, there were 125 individuals with missing data on the delinquent peers variable. This consisted of 48 females and 77 males, and 104 Whites and 21 African Americans. For the other variables the number of individuals with missing data increased over time. This is probably due to attrition as well as with adolescents not wanting to give information on sensitive topics such as violent behavior, drug use and victimization experiences. However, for the delinquent peers variables, the number of individuals with missing data followed a different pattern with the number increasing from wave one to wave two, followed by a decrease in wave three and an increase in the number of individuals with missing data at wave four. There could be several reasons for the difference in the pattern of missing data on the delinquent peers variables. One reason is that in order to answer this question at waves one and two, a respondent must first have given information about peers in a previous question. For waves three and four, the respondents were asked questions regarding delinquent activity of their peers regardless of whether they gave the initials of their peers in a previous question. This could actually allow more adolescents to respond to the questions about delinquent peers at waves three and four. Table 4.8 gives descriptive information about the delinquent peers variables after excluding the missing data. It shows that, on average, association with delinquent peers increased slightly over time for the entire panel of 1,203 individuals used in the present study. African Americans reported greater association with delinquent peers for the


65

first and fourth waves of the NYS. Whites reported only a slightly higher association on the second and third waves. Males reported a greater association with delinquent peers than females for all waves of the NYS. Table 4.8. Means, standard deviations, and variances for delinquent peers Total

Race Black

White

Gender Male

Female

N Mean S.D. Var.

DPEER1 1054 8.39 2.50 8.54

DPEER2 1048 8.41 3.20 10.24

DPEER3 1136 8.85 3.24 10.49

DPEER4 1078 8.93 3.39 19.84


163 9.09 3.14 14.99 891 8.26 2.70 7.27

161 8.40 3.14 9.87 889 8.41 3.21 10.32

182 8.83 3.41 11.61 954 8.85 3.21 10.29

179 9.37 4.45 19.84 899 8.85 3.13 9.82


537 8.99 3.20 10.23 517 7.76 2.46 6.03

540 9.06 3.53 12.49 508 7.71 2.63 6.92

598 9.41 3.37 11.38 538 8.22 2.96 8.77

556 9.56 3.66 13.40 522 8.26 2.94 8.63

Control Variables Other variables are used in the analyses presented here. These variables were created using items on the questionnaires of the NYS. One is a variable representing gender (called GENDER) with males coded 1 and females coded 0. Another was one representing race. The adolescents were asked “Which one of these groups best describes you?” The answer choices were “Anglo,” “Black,” “Mexican American,” “Spanish American,” ”Chicano,” “American Indian,” “Asian,” and “Other.” As stated earlier, because there were too few cases within each of the latter six categories to make reliable comparisons within multivariate models, these analyses will only use individuals who reported

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that they were either “Anglo” or “Black” with African Americans being coded 1 and Whites being coded 0 on a variable called RACE. Table 4.9 gives information on most of the control variables used in this analysis. It shows that slightly more than half of the sample was male, and that the majority was White (83%) with African Americans taking up 17% of the remaining sample. To examine the effects of negative stimuli and the other independent variables across gender and racial groups, analyses were conducted separately by gender and race. Table 4.9. Percentages for race, gender and SES Percentage Race (N=1,203) African-American White Gender (N=1,203) Males Females SES (N =1,145) $6,000 and under $6,001-$10,000 $10,001-$14,000 $14,001-$18,000 $18,001-$22,000 $22,001-$26,000 $26,001-$30,000 $30,001-$34,000 $34,001-$38,000 $38,001 and above

16.6 83.4 52.6 47.4 12.5 12.2 16.4 17.7 14.8 11.4 5.6 3.3 1.8 3.8

Socioeconomic status was also controlled in this study. As stated earlier, for the first wave of the National Youth Survey, one parent or adult guardian was interviewed along with the adolescent. During this “parent” interview, adults were asked, “What would you say was the approximate total family income last year?” The response choices were coded 1-10 for the categories: “$6,000 and under,” “$6,001 to $10,000,” “$10,001 to $14,000,” “$14,001 to $18,000,” “$18,001 to $22,000,” “$22,001 to $26,000,” “$26,001 to $30,000,” “$30,001 to $34,000,” “$34,001 to $38,000,” and “$38,001 and more.” These responses were coded onto a variable entitled SES. Higher values indicated higher income categories. Also, Table 4.9 shows that more than half of the adults interviewed along with the White and African


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American adolescents reported that the total family income was less than $22,000 a year. Further analyses revealed that on average, males had a slightly lower average on the SES variable than females (4.13 vs. 4.33). Also, on average, African Americans (2.48) came from families with lower household incomes in 1976 than Whites (4.57). As with several other variables aforementioned in this chapter, there was some missing information on the SES variable. From the entire panel used in this study, 58 individuals had missing data on the SES variable. This consisted of 29 males and 29 females. This group also consisted of 13 African Americans and 45 Whites. The fourth control variable was age. All adolescents in the NYS were asked the question, “How old are you?” at each wave of the study. This age was recorded for each of the first four waves of the NYS and used as a control variable in the present study. This variable was called AGE1 at wave one. It was called AGE2 at wave two and so on. Table 4.10 shows the age and breakdown of the panel used in the present study at each of the four waves. It shows that gender and racial groups in the sample were very close with respect to their ages. Table 4.10. Means, standard deviations, and variances for age Total Race Black White Gender Male Female

Mean S.D. Var.

AGE1 13.01 1.40 1.97

AGE2 14.01 1.40 1.97

AGE3 15.01 1.40 1.97

AGE4 16.01 1.40 1.97

Mean S.D. Var. Mean S.D. Var.

12.97 1.34 1.80 13.01 1.42 2.01

13.97 1.34 1.80 14.01 1.42 2.01

14.95 1.34 1.80 15.01 1.42 2.01

16.97 1.34 1.80 16.01 1.42 2.01

Mean S.D. Var. Mean S.D. Var.

13.07 1.40 1.97 12.93 1.40 1.97

14.07 1.40 1.97 13.93 1.40 1.97

15.07 1.40 1.97 14.93 1.40 1.97

16.07 1.40 1.97 15.93 1.40 1.97

Another variable that was used in the analysis was called WAVE. This is a variable that was used to designate a particular wave in the study. For growth curve modeling, the statistical tool used in this study,

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it was necessary to have some variable in each model to designate time. Every individual has the same value on WAVE at each wave of the NYS. At wave one, WAVE equaled 0 for all individuals. At wave two, WAVE equaled 1. At wave three, this variable equaled 2. At wave four, this variable equaled 3. Interaction Terms Part of Agnew’s (1992) explanation of general strain theory stipulated that the effect of strain was conditioned by several factors such social support. In order to test this portion of GST, several interaction variables, representing the moderating effect of the conditioning variables on strain were created. There were four interactions created: the interaction of victimization and delinquent peers, the interaction of victimization and goals, the interaction of victimization and conforming values, and the interaction of victimization and social support. To create these interactions in the data, first z-scores of the values on the victimization and the conditioning factors at each of the four waves were calculated. This was done to decrease the likelihood of multicollinearity later in the multivariate analyses. Models were then estimated with the interaction and its components separately. In other words, the interaction and its components were in the same model. The method of placing an interaction and its separate components in the same model has been used by other criminologists. For example, in a 2002 article, Agnew and his colleagues used strain and negative emotionality separately in the same model as the interaction of strain and negative emotionality. Even though they were using regression and the present study uses hierarchical models, the practice of putting an interaction and its components in the same model does have some precedent in the criminological literature. Next for each adolescent in the present study, the z-score on the victimization variable was cross multiplied with the z-score on a variable of one of the conditioning factors in the same wave. For example, for each adolescent in this study, the z-score of the victimization wave one variable (VICT1) was cross multiplied with the z-score of the delinquent peers wave one variable (DPEER1) to create an interaction variable (VDPEER1) representing the interaction of victimization and delinquent peers at wave one. There were four interaction terms, one for each wave, for each of the four interactions representing a total of sixteen interaction terms used in this study. The interaction of


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victimization and delinquent peers was represented by VDPEER1 at wave one, VDPEER2 at wave two, VDPEER3 at wave three, and VDPEER4 at wave four. The interaction of victimization and goals was represented by VGOALS1 at wave one, VGOALS2 at wave two, VGOALS3 at wave three, and VGOALS4 at wave four. The interaction of victimization and conforming values was represented by VVALUES1 at wave one, VVALUES2 at wave two, VVALUES3 at wave three, and VVALUES4 at wave four. The interaction of victimization and social support was represented by VSUPPT1 at wave one, VSUPPT2 at wave two, VSUPPT3 at wave three, and VSUPPT4 at wave four. A statistically significant (p < .05) or (p < .01) coefficient estimated for an interaction term was interpreted one of two ways in this study. One way was that the exponentiated beta (eβ) was taken for the coefficient of the interaction. This value (eβ) was interpreted as the factor by which the outcome is multiplied with a unit increase on the interaction term. The second way was inserting the coefficient into the equation ((eβ - 1) * 100). The result of this equation was the percentage by which the outcome changed over time. This value represented a percentage increase if the coefficient was positive and represented a percentage decrease if the coefficient was negative. The use of interaction terms in hierarchical models, as was done in the present study, is rare. However, there is some precedent for this in the work of Weidner and his colleagues (2004). They used interaction terms in a similar fashion to what was done in this study.

RELIABILITY AND THE EFFECT OF MISSING DATA As shown above there were several scales that were constructed to measure various variables used in the present study (goals, social support, conforming values, delinquent peers, nonviolent/non-drug offending, violent offending, drug use, and victimization). Before the hypotheses of this study were tested, reliability analyses were conducted on measures used in this study. Cronbach’s alpha was calculated for each of these nine scales at wave one. Table 4.11 shows the Cronbach’s alpha for these scales measuring the conditioning factors at wave one.

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Table 4.11. Cronbach’s Alphas for scales measuring conditioning factors at Wave 1 Total Race Black White Gender Male Female

GOALS1 .58

SUPPT1 .53

VALUES1 .78

DPEER1 .71

.51 .60

.46 .54

.79 .77

.78 .68

.60 .56

.52 .55

.80 .72

.73 .56

Table 4.11 shows that for the variables representing the various constraints to delinquent coping, the scales representing these factors were found mostly to be reliable (a >.50). The only exception is the Cronbach’s alpha of .46 for African Americans on the social support scale at wave one. Also, the goals scale was found to be more reliable for Whites than African Americans and slightly more reliable for males than females. The social support scale was found to be more reliable for Whites than African Americans and more reliable for females than for males. Furthermore, the scale measuring conforming values was slightly more reliable for African Americans than Whites and more reliable for males than females. Also the scale measuring association with delinquent peers was more reliable for African Americans and males compared to their respective counterparts. Table 4.12 gives reliability information regarding the three outcome variables and victimization at time one. Values of Cronbach’s alpha for these variables were taken after outliers were transformed on VICT1, NONVIO1, and VIOLENT1. When looking at the victimization scale at wave one, Table 4.12 reveals a very low Cronbach’s alpha. However, one of the goals of this study is to measure as much victimization and offending behavior as possible; that is a multidimensional scale. A multidimensional scale will not yield a high alpha because it is not intended to (Traub 1994). For example, the victimization scale includes questions about rape and physical assault by parents. As far as this researcher knows, there is no research that states that a respondent who endures rape will necessarily be more likely to be assaulted by their parents as well. Since the goal of this measure is to capture as many dimensions of victimization as possible within a given type (e.g. violent), the low level of alpha is acceptable.


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Table 4.12. Cronbach’s Alphas for scales measuring victimization, nonviolent/non-drug offending and illicit drug use at Wave 1 Total Race Black White Gender Male Female

VICT1 .07

NONVIO1 .53

VIOLENT1 .60

DRUGS1 .94

.30 .06

.56 .42

.55 .61

.96 .94

.31 .04

.53 .07

.61 .51

.92 .98

Table 4.12 also shows variable reliability for the nonviolent/non-drug offending. The scale was much more reliable for males than for females. It was also more reliable for African Americans than Whites. The violent offending scale was found to be reliable (a >.50) for the entire panel, and all racial and gender groups with it being more reliable for Whites and males. Furthermore, the scale created to measure drug use was found to be very reliable (a > .90) for the entire panel of 1,203 individuals used in this study and for all racial and gender groups within that panel. As stated earlier, a major drawback to using the National Youth Survey, and any longitudinal study, is that there is the possibility of attrition occurring during the course of the survey. This leads to missing data from those who have dropped out of the study. Furthermore, some respondents may have been reluctant to answer certain questions on the questionnaire that also leads to missing data on those particular questions. The effect of missing data was considered in the present study. Analyses were conducted to determine if there were any differences between those who had complete data for all four waves and those who had dropped out later in the study and therefore had missing data at waves two through four. There was generally very little difference, on average, between those who had complete data and those who had incomplete data in terms of reported victimization, goals, conforming values, drug use, age and association with delinquent peers. However, among those who had incomplete data, a majority were male, White, lived in families with a lower household income, and were involved, on average, in more violent offending and nonviolent/non-drug offending than those with complete data. Therefore, the results of this study must be viewed with caution. A more detailed description of the analysis of missing data can be found in the Appendix.

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ANALYTICAL TECHNIQUES The statistical tool that was used to test the validity of the aforementioned hypotheses was growth curve modeling. The use of growth curve modeling has increased tremendously over the past two decades to measure change over time when using longitudinal data. According to Bryk and Raudenbush (1987), this technique can deal with various issues of longitudinal data that previous research using other statistical methods could not handle. One issue is conceptual. Prior to the introduction of growth curve modeling, research on individual change rarely conceptualized an explicit model of individual change. With growth curve modeling, one can create a model for individual growth. Another issue of previous techniques for longitudinal data deals with measurement. Bryk and Raudenbush (1987) noted that studies of individual change prior to the introduction of growth curve modeling had previously employed procedures that were developed to differentiate between individuals at a fixed point in time. In other words, they were developed mainly for cross-sectional data. These statistical methods, they argued, used techniques such as standardizing variances and means, which essentially eliminated individual change. They claimed that growth curve modeling was specifically designed to allow for means and variances to vary over time. A third issue of longitudinal research conducted prior to the introduction of growth curve modeling deals with design. For most studies where data have been collected on more than two occasions, researchers have usually analyzed the data as a series of separate designs with two time points. Growth curve modeling allows for the examination of longitudinal data with more than two data points in one model (Bryk and Raudenbush 1987). Growth curve modeling can be viewed as a version of multilevel modeling or hierarchical generalized linear modeling (HGLM).7 HGLM deals with the analysis of “nested” data. Nested data usually consists of information from people that are members of groups. The individuals are described as being nested in those groups. The individuals are seen as level-1 units and the groups that they are members of are noted as level-2 units. If the level-2 units are apart of larger groups, these large groups are seen as level-3 units and so on (Raudenbush and Bryk 2002).


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According to Raudenbush and Bryk (2002), in applying HGLM to a multilevel data structure or nested data, each level of data has its own model or set of equations. For a 2 level data structure, there would be 2 models. For the level-1 model, there are three parts: the sampling model, link function and structural model. The sampling model designates how the level-1 outcome variable is distributed (normally, binomial, etc.). For example, with a level-1 outcome that is normally distributed, the level-1 sampling model would be: Yij | µij ~ NID (µij, σ2)

[4.1]

Equation 4.1 shows that the level-1 outcome, Yij, given a predicted outcome value, µij, is normally and independently distributed with an expected value of µij and a constant variance, σ2. The link function at level-1 is an equation that allows for the transformation of the level-1 predicted outcome, µij, so that the predictions are constrained to lie within a certain interval (Raudenbush and Bryk 2002). In the normal case, there is no transformation so the link function is as follows: ηij = µij

[4.2]

Here, ηij is the transformed predicted value. For the normal case, the transformed value is the same as the predicted value. In this case, the link function is called the identity link function. As will be shown later, other types of distributions will require different transformations (Raudenbush and Bryk 2002). The third part of the level-1 HGLM model is the structural model. The structural model shows how the transformed value, ηij, is related to level-1 predictors. In the normal case, the transformed predicted value, ηij, is seen as the linear function of level-1 predictors (Raudenbush and Bryk 2002). ηij = β0j + β1jX1ij + β2jX2ij + … + βpjXpij

[4.3]

For the level–2 model, each of the parameters in the level-1 model is used as an outcome variable. Each of these outcome variables is predicted by another set of parameters that include level-2 predictors or group level characteristics (Raudenbush and Bryk 2002). HGLM can be modified to accommodate several different types of data. One type of data that HGLM can handle is count data where, for example, the outcome is the number of violent crimes a person has committed during a particular year (Raudenbush and Bryk 2002). For

74


the three part level-1 model for count data, the sampling model would be a Poisson sampling model as shown below: Yij | λij ~ P (mij, λij)

[4.4]

Here, the outcome, Yij, has a Poisson distribution with an exposure, mij, which is the interval of time that an outcome can occur and an event rate, λij, which is the rate per time period at which the outcome occurs. In this case, with the Poisson distribution, the expected value and variance of the outcome, Yij, given the event rate is: E(Yij | λij) = mijλij

[4.5]

Var(Yij | λij) = mijλij

[4.6]

Equation 4.5 shows that the expected number of events for unit i in group j is equal to its event rate times its exposure. Also, equation 4.6 shows that the variance of Yij is equal to this as well. An example using concepts from criminology is the count of the number of crimes committed during one year for each person i. Here, the exposure is set equal to one. In this case, the expected value of the outcome is simply the event rate. The exposure can be constant or not constant. A constant exposure means that everyone in the sample has the same amount of time of scoring on the dependent variable. A nonconstant exposure means that not everyone has the same amount of time to score on the dependent variable (Raudenbush and Bryk 2002). In the event of nonconstant exposure, the researcher would usually have a variable that predicts who had more exposure and factor that into the analyses. In the present study, however, the exposure was assumed to be constant for all individuals since all of the NYS adolescents were asked about their delinquent behavior for the previous 12 months at each of the first four NYS waves. The link function used when dealing with count data and a Poisson sampling distribution is the log link function that is represented by: ηij = log(λij)

[4.7]

Here, the transformed expected outcome, ηij, is the log of the event rate. The level-1 structural model using count data is the same equation 4.3. The only difference is that in the case of normally distributed data, the βs in the equation represent change in the outcome with a one unit change in a particular independent variable (Raudenbush and Bryk 2002).


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In the case of count data with a Poisson distribution, a β in the equation is interpreted in a way similar to coefficients estimated in a Poisson regression model. A β in the equation represents the log of the factor by which the outcome is multiplied with a unit change in a particular independent variable. The log of the factor by which the outcome is multiplied for a unit change in a predictor can be converted to the factor by which the outcome is multiplied by computing an exponentiated beta (eβ) (Gardner et al. 1998). The β can be transformed into a percentage change in the outcome by calculating ((eβ - 1) *100). If the β is negative, the result of ((eβ - 1) *100) represents the percentage by which the outcome decreases. If the β is positive, the result of ((eβ - 1) *100) represents the percentage by which the outcome increases (Gardner et al. 1998). The level-2 model is the same as explained earlier, where the parameters of the level-1 structural model are seen as outcomes at level-2. These outcomes are predicted by level-2 characteristics (Hoffman and Cerbone 1999). The use of HGLM can be further applied to data that is also longitudinal, as is the case with the NYS data used here. Longitudinal data is information taken from a group of people who are followed over a set period of time. Information is collected from these people at several points during this period of time. This information that is collected could be count data, for example, the number of property offenses committed by a person could be collected at each time point (Hoffman and Cerbone 1999). Whether the longitudinal data is count or continuous, it is analyzed using HGLM, and is usually referred to as growth curve modeling. This is because when a person’s status on an outcome variable at every time point is plotted, the graph usually represents a curve (Bryk and Raudenbush 1987). Growth curve modeling with longitudinal data has been used in several areas of the social sciences including criminology (Raudenbush and Chan 1992). It has been used with longitudinal data that is also count data (Hoffman and Cerbone 1999). It has also been more frequently used with longitudinal data that is also assumed to be continuous (Raudenbush and Chan 1992). Since the present study deals with longitudinal that are assumed to be count data and other longitudinal data that are assumed to be continuous, the application of growth curve models to both will be explained here. First, growth curve modeling using HGLM with longitudinal count data will be explained.

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Later, growth curve modeling using HGLM with longitudinal continuous data will be explained. When estimating growth curve models using longitudinal count data, multiple observations of a person over time are viewed as being nested in that individual. The individual observations are seen as level1 units and the people are viewed as level-2 units. Most longitudinal data have two levels (within person and between persons). Therefore, this type of data can usually be represented with a 2 level model (Bryk and Raudenbush 1987). Similar to Bryk and Raudenbush’s explanations about growth curve models with a continuous outcome, at level-1 or the within person model, Yti is understood to be the observed status on the outcome variable at time t for person i. It is seen as a function of a systematic growth trajectory or growth curve. Unlike with a continuous outcome, there is no random error with count data in the level-1 (within-person) model. This growth over time can be represented as a polynomial of a degree P. Then, the level-1 equation is as follows: Yti = β0i + β1iati + β2ia2ti +……..+ βPiaPti

[4.8]

Here, i equals 1,………, n subjects and each subject is observed on t occasions. ati is the age of the subject at time t for person i. This is essentially the level-1 structural model mentioned earlier for the HGLM level-1 model for count data. Where, Yti is the same as the transformed value of ηij . In equation 4.8, β0i is the intercept of the level-1 equation and is a parameter that represents the log of the status of a person on the outcome variable at time 0. The status on the outcome at time 0 can be found by calculating the exponentiated beta (eβ0i). The outcome of the exponentiated beta (eβ0i) could represent the initial status on the outcome of a person if time 0 was the first time point in the study. However, the researcher could code the time points of a longitudinal dataset so that time 0 could be any other time point. Furthermore, βpi is the slope of the level-1 equation and represents the growth trajectory parameter p for subject i associated with the polynomial of degree P. This slope indicates the growth rate in the curve. It is interpreted as the log of the factor by which the outcome is multiplied between two time points. The factor by which the outcome is multiplied can be determined by calculating the exponentiated beta (eβpi). Also, the percentage by which the outcome changes at each time


77

point can be found by calculating ((eβpi - 1) * 100). If βpi was positive, the result of ((eβpi - 1) * 100) represents the percentage by which the outcome increases between two time periods. If βpi was negative, the result of ((eβpi - 1) * 100) represents the percentage by which the outcome decreases between two time points. The polynomial, βPiaPti, represents the shape and curvature of a growth curve. The polynomials used in growth curve models are usually quadratic. Other terms can be included in the level-1 model besides the slope, intercept and the polynomial term. These other terms are called time varying covariates. These are variables other than age or time that can help to explain variation in the outcome variable. These variables vary across time so they are seen as “nested” within an individual and therefore included in the level-1 equation. (Bryk and Raudenbush 1987; Raudenbush and Bryk 2002). The level-2 or between persons model usually shows how individual level characteristics affect the intercept and slopes in the level-1 equation. For a quadratic level-1 equation, the level-2 equations would be: β0i = γ00 + γ01X0i + u0i

[4.9]

β1i = γ10 + γ11X1i + u1i

[4.10]

β2i = γ20 + γ21X2i + u2i

[4.11]

With an outcome that has a Poisson distribution, from equation 4.9, eγ00 is the mean status on the outcome at time 0. eγ10 is the average mean growth rate. eγ20 is the mean acceleration or deceleration rate in the growth curve. The X’s represent level-2 predictors. Predictors at level-2 in a growth curve model are usually individual level characteristics such as race or gender. Calculated from γ01 in equation 4.9, ((eγ01 – 1) * 100) represents the percentage change in the initial status with a unit change in X0i. Calculated from γ11 in equation 4.10, ((eγ11 – 1) * 100) is interpreted as the percentage change in the growth trajectory with a unit change in the predictor. Also, ((eγ21 – 1) * 100) is interpreted as the percentage change in the deceleration/acceleration in the growth curve with a unit change in the predictor. Time varying covariates included at level-1 are also specified at level-2. They become outcomes at level-2 like other level-1 parameters. Raudenbush and Bryk (2002) argue that it is customary to specify

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covariates as fixed (i.e. β3i = γ30). However, covariates could also be allowed to vary (i.e. β3i = γ30 + u3i). If the outcome is continuous, a statistically significant coefficient estimated for a time varying covariate is interpreted as change in the dependent variable over time with one unit change in the independent variable the coefficient is associated with. If the outcome is count data, a statistically significant coefficient estimated for a time carrying covariate is interpreted as change over time of the log of the factor by which the outcome is multiplied with a unit change in the independent variable. This can be changed to a percentage change in the outcome over time by calculating ((eβ - 1) * 100). There is another issue that is associated with dealing with count data with a Poisson distribution. An assumption of a variable with a Poisson distribution is that its variance is equal to its mean. In looking at the descriptive statistics of the dependent variables in the present study, it is obvious that this assumption is not upheld in this study. With every dependent variable used in this study, the variance is larger than the mean. This is what Hoffman and Cerbone (1998) call overdispersion. Failing to consider overdispersion when assuming a Poisson distribution of the dependent variable produces levels of statistical significance that are incorrect. In the present study, the statistical software used to estimate the models, HLM for Windows Version 5, contained an option where overdispersion of the dependent variable is considered when estimating an HGLM model. This option was used in models with the two dependent variables that were count data (violent offending and nonviolent/non-drug offending) of the present study to account for the unequal variance and means of these two dependent variables. Now, turning to the method used for estimating models predicting illicit drug use assuming a continuous outcome, applying growth curve modeling to longitudinal data that is considered continuous is very similar to the aforementioned method applied to count data. At level-1 or the within person model, the model can be seen as follows: Yti = β0i + β1iati + β2ia2ti + β PiaPti + rti

[4.12]

As with count data, Yti is the observed status on the outcome variable at time t for person i. It is a function of a set of parameters plus random error. β0i is the intercept of the level-1 equation and is a parameter that represents the status of a person on the outcome variable


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at time 0. β1i is the slope of the level-1 equation and represents the growth trajectory for person i. This slope indicates the growth rate in the curve. The polynomial, β PiaPti, represents the shape and curvature of a growth curve. (Bryk and Raudenbush 1987; Raudenbush and Bryk 2002). In the case of a continuous outcome, the level-2 or between persons model usually shows how individual level characteristics affect the intercept and slopes in the level-1 equation. For a quadratic level-1 equation mentioned, the level-2 equations would be: βPiaPti 0i = γ00 + γ 01X0i + r0i

[4.13]

βPiaPti 1iati

= γ10 + γ 11 X1i + r1i

[4.14]

βPiaPti 2ia2ti

= γ20 + γ 21 X2i + r2i

[4.15]

In equations 4.13, 4.14, and 4.15, γ 00 is the mean status on the outcome at time 0. γ10 is the average change is status from one time point to the next, or the mean growth rate. γ 20 is the mean acceleration or deceleration rate in the growth curve. The X’s represent a level-2 predictor. In the equation for status at time 0, β0i , γ 01 represents the effect that a predictor has on the status at time zero. With a continuous dependent variable, it is interpreted as the change in the status at time 0 with a unit change in the predictor. γ11 is interpreted as the change in the growth trajectory with a unit change in the predictor. Also, γ 21 is interpreted as the change in the deceleration/acceleration in the growth curve with a unit change in the predictor. The models predicting illicit drug use were estimated using HLM 5 for Windows. Since illicit drug use is considered a continuous variable in the present study and not a count variable, concerns regarding overdispersion and constant exposure are not considered.

ANALYSIS PLAN Since two of the dependent variables, violent offending and nonviolent non-drug offending, were measured as count variables and are highly skewed, it was reasonable to view them as distributed as a Poisson distribution (Osgood and Rowe 1994). As a result, each in every equation in which either violent offending or nonviolent/non-drug offending was the outcome, the dependent variable was assumed to have a Poisson distribution. This was taken into consideration when the

80


models were estimated. As a result, for this study’s analyses, growth curve models with an HGLM approach from count data were estimated. As for illicit drug use, as mentioned earlier, it was assumed to have a continuous distribution. It was not count data due to the fact that it did not consist of count the number of things, rather collecting the amounts of frequencies of illicit drug use. As mentioned earlier, all growth curve models were estimated using a statistical software program called HLM for Windows Version 5 (Raudenbush et al. 2000). Also, listwise deletion was used in every growth curve model estimated for this study. Random-Coefficient Regression Models There were a number of growth curve models that were estimated in this study. First, there were several random-coefficient regression growth curve models estimated to gain baseline characteristics such as the initial status of a person on an outcome variable. Randomcoefficient regression models are models that contain no level-2 predictors and a simple linear equation at level-1 (Raudenbush and Bryk 2002). The random regression models estimated in the present study were as follows: three (one per dependent variable) models for the entire panel of 1,203 individuals in this study, three models for African Americans in the panel, three models for Whites in the panel, three models for males in the panel, and three models for females in the panel. The level-1 and level-2 equations for the random regression models for the models predicting violent offending and nonviolent/nondrug offending were as follows: Level-1: Yti = β0i + β1i(WAVE)ti

[4.16]

Level-2: β0i = γ00 + u0i

[4.17]

β1i = γ10 + u1i

[4.18]

In equation 4.16, Yti represents the expected value on the outcome variable at a particular point in time for a particular individual. It is seen as a function of an intercept, and a growth rate. As shown in equations 4.17 and 4.18, both the intercept and the growth rate were used as outcome variables at level-2. From these two equations, 4.17 and 4.18, two baseline statistics can be obtained, γ00, which is the mean initial status on the outcome variable, and γ10, which is the mean growth rate.


81

For the models predicting illicit drug use, the level-1 equation of the random coefficient regression models looked similar to that of the models predicting the other two outcomes in the study. The only difference is that in the level-1 equation of the random coefficient regression models predicting illicit drug use, there is an error term (rti) on the end of the right side of the equation. The level-2 portion of the random coefficient regression models was the same as equations 4.17 and 4.18 above. Intercepts-and-Slopes-as-Outcomes Models To test the efficacy of the constructs from general strain theory, there were several intercepts-and-slopes-as-outcomes growth curve models estimated. Intercepts-and slopes-as-outcomes models are those models that have predictors in both levels. For the entire panel of 1,203 individuals involved in the present study, there were five two-level intercepts and slopes-as-outcomes models estimated: one model used to test each of the four types of interactions and a control model with no interaction terms. These five two-level models that were estimated for each of the dependent variables along with the three random regression models estimated (one per dependent variable) created a total of eighteen models estimated for the entire panel. The level-1 equation in models predicting nonviolent/non-drug offending and violent offending, for the control model for the entire panel consisted of the following equation: Yti = β0i + β1(WAVE)ti + β2i(AGE)ti + β3i(VICT)ti + β4i(DPEER)ti + β5i(GOALS)ti + β6i(VALUES)ti + β7i(SUPPT)ti

[4.19]

In equation 4.19, the outcome variable was a function of several aspects. It was a function of an intercept (β0i), growth trajectory (β1i), and several time varying covariates representing age, victimization, association with delinquent peers, goals, conforming values, and social support. The level-2 portion of the control model consists of equations that see the predictors in the level-1 equation as outcome variables. The intercept and growth trajectory are predicted by an individual’s race, gender and score on the SES variable. All of the other time-varying covariates are specified as fixed. The level-2 equations for the control model were as follows: β0i = γ00 + γ01(GENDER)0i + γ02(RACE)0i + γ02(SES)0i + u0i

[4.20]

82


β1i = γ10 + γ11(GENDER)0i + γ12(RACE)0i + γ12(SES)0i + u1i

[4.21]

β2i = γ20

[4.22]

β3i = γ30

[4.23]

β4i = γ40

[4.24]

β5i = γ50

[4.25]

β6i = γ60

[4.26]

β7i = γ70

[4.27]

The four models testing the interaction of victimization with the various constraining factors were very similar to the control model. For the entire sample of 1,203 individuals in this study, in models predicting nonviolent/non-drug offending and violent offending, the following equation was the level-1 one model in model testing the interaction of victimization and delinquent peers: Yti = β0i + β1(WAVE)ti + β2i(AGE)ti + β3i(VICT)ti + β4i(DPEER)ti + β5i(GOALS)ti + β6i(VALUES)ti + β7i(SUPPT)ti + β8i(VDPEER)ti

[4.28]

Equation 4.28 is very similar to equation 4.19. The only difference is that equation 4.28 has a term to represent the interaction between victimization and association with delinquent peers. As a result of the addition of this time varying covariate, at level-1, this variable becomes an outcome and is specified as fixed at level-2: β8i = γ80

[4.29]

Equations 4.20 through 4.27 and equation 4.29 constitute the level2 portion of a model testing the interaction between delinquent peers and victimization for the entire panel of 1,203 adolescents. To test the interaction between victimization and goals for the entire sample of 1,203 individuals in this study, in models predicting nonviolent/nondrug offending and violent offending, the following equation was the level-1 model was used: Yti = β0i + β1(WAVE)ti + β2i(AGE)ti + β3i(VICT)ti + β4i(DPEER)ti + β5i(GOALS)ti + β6i(VALUES)ti + β7i(SUPPT)ti + β8i(VGOALS)ti

[4.30]


83

Equation 4.30 is very similar to equation 4.28. The only difference is that equation 4.30 has a term to represent the interaction between victimization and one’s goals for the future instead of the interaction between victimization and association with delinquent peers. The victimization/goals interaction term becomes fixed at level-2, which produces equation 4.29: β8i = γ80

[4.29]

Along with equations 4.20 through 4.27 and equation 4.29 constitute the level-2 portion of a model testing the interaction between one’s goals and victimization for the entire panel of 1,203 adolescents. To test the interaction between victimization and conforming values for the entire sample of 1,203 individuals in this study, in models predicting nonviolent/non-drug offending and violent offending, the following equation was the level-1 model was used: Yti = β0i + β1i(WAVE)ti + β2i(AGE)ti + β3i(VICT)ti + β4i(DPEER)ti + β5i(GOALS)ti + β6i(VALUES)ti + β7i(SUPPT)ti + β8i(VVALUES)ti

[4.31]

Equation 4.31 is very similar to equations 4.28 and 4.30. The only difference is that equation 4.31 has a term to represent the interaction between victimization and one’s values towards deviant behavior. The victimization/conforming values interaction term becomes fixed at level-2, which again produces equation 4.29: β8i = γ80

[4.29]

Along with equations 4.20 through 4.27 and equation 4.29 constitute the level-2 portion of a model testing the interaction between one’s values towards deviant behavior and victimization for the entire panel of 1,203 adolescents. To test the interaction between victimization and social support for the entire sample of 1,203 individuals in this study, in models predicting nonviolent/non-drug offending and violent offending, the following equation was the level-1 model was used: Yti = β0i + β1i(WAVE)ti + β2i(AGE)ti + β3i(VICT)ti + β4i(DPEER)ti + β5i(GOALS)ti + β6i(VALUES)ti + β7i(SUPPT)ti + β8i(VSUPPT)ti

[4.32]

84


Equation 4.32 is very similar to equations 4.28, 4.30, and 4.31. The only difference is that equation 4.32 has a term to represent the interaction between victimization and one’s social support. The victimization/social support interaction term becomes fixed at level-2, which again produces equation 4.29: β8i = γ80

[4.29]

Along with equations 4.20 through 4.27 and equation 4.29 constitute the level-2 portion of a model testing the interaction between one’s social support and victimization for the entire panel of 1,203 adolescents. For the models predicting illicit drug use, the level-1 equation of all of the intercepts-and slopes as outcomes models looked similar to that of the models predicting the other two outcomes in the study. The only difference is that in the level-1 equation predicting illicit drug use, there is an error term (rti) on the end of the right side of the equation. The level-2 portions were the same as in models predicting the other two outcomes. For each racial group in the panel used in this study, there were five intercepts and slopes-as-outcomes models estimated: one model used to test each of the four types of interactions and a control model with no interaction terms. These five two-level models that were estimated for each of the dependent variables along with the three random regression models (one per dependent variable) created a total of eighteen models estimated for each racial group in the panel. The equations used at each level of each model for each racial group were the same as those used for the entire panel with one exception: there was no race variable used in the models for each racial group. For each gender group in the panel used in this study, there were five intercepts and slopes-as-outcomes models estimated: one model used to test each of the four types of interactions and a control model with no interaction terms. These five models that were estimated for each of the dependent variables in addition to the three random regression models estimated (one per dependent variable) created a total of eighteen models estimated for each gender group in the panel. The equations used at each level of each model for each gender group were the same as those used for the entire panel with one exception: there was no gender variable used in the models for each gender group.

CHAPTER 5

Adolescent Victimization and Violent Offending

FULL PANEL As stated earlier, several growth curve models predicting violent offending were estimated for this study. For the full panel, there were six models estimated predicting violent offending. First, a random coefficient regression model was estimated with no level-2 predictors and only one predictor (WAVE) at level-1. Table 5.1 gives information from this model. It shows that the mean log number of violent offenses committed at the first wave of the NYS was 0.17 for the full panel. This translates into an average of 1.18 (e.17) violent offenses at wave 1. The coefficient -.19 in the random coefficient regression model shows that for each wave after wave one, the number of violent offenses committed by the full panel decreased, on average, by 21% ((e-.19 – 1) * 100). The control model shows that gender was very important in determining the amount of violent offending at wave one for the full panel in the present study. More specifically, being male multiplied the number of violent offense committed by the sample by a factor of 2.18 (e.78), controlling for the effect of all other variables. Also, males had 118% ((e.78 – 1) *100) more violent offenses at wave one than females, controlling for the effect of all other variables. Also, SES was found to be significant (p < .01) in predicting β1. Table 5.1 shows that each unit increase on the SES scale decreases the change from wave to wave in the number of violent offenses by about 3%. Therefore, adolescents from families with a higher household income in 1976 had a smaller change in the number of violent offenses from wave to wave than adolescents from families with lower household incomes. 85

86


Table 5.1. Random coefficient regression and control models predicting violent offending, full panel, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 RACE, γ02 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 RACE, γ12 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70

Control

.17 (.08)*

1.29 (.89)** .78 (.12)** .28 (.16) -.06 (.02)

-.19 (.03)**

-.11 (.07) -.07 (.05) -.04 (.07) -.03 (.01)* .03 (.03) .06 (.02)* .09 (.02)** -.04 (.06) -.09 (.02)** -.06 (.03)

*p< .05 **p .05).


99

Table 5.9. Random coefficient regression and control models predicting violent offending, females, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

.69 (.08)**

-.01 (.04)*

Control .50 (1.41) -.06 (.19) -.05 (.03) .02 (.08) .12 (.09) -.03 (.01)* -.08 (.05) .07 (.08) .14 (.03)** .05 (.09) -.02 (.04) -.09 (.06)

100


Table 5.10. Models with interaction terms predicting violent offending, females, coefficients (standard errors) VDPEER Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VGOALS

VVALUES

-.30 (1.23) .05 (.18) -.03 (.03)

.41 (1.39) -.05 (.19) -.05 (.03)

.99 (1.41) .05 (.19) .05 (.03)

.72 (1.41) .05 (.19) .05 (.03)

.14 (.09) .05 (.10) -.05 (.01)*

.03 (.08) .12 (.09) -.04 (.01)*

.03 (.08) .13 (.09) .03 (.01)*

.03 (.08) .12 (.09) .03 (.01)*

-.07 (.05)

-.07 (.05)

.07 (.05)

.08 (.05)

.14 (.03)**

.08 (.09)

.09 (.06)

.07 (.08)

.18 (.02)**

.15 (.02)**

-.04 (.06)

.03 (.07)

.05 (.09)

.05 (.09)

-.01 (.04)

-.01 (.04)

.04 (.04)

.02 (.04)

-.04 (.04)

-.09 (.06)

.09 (.06)

.10 (.06)

.14 (.02)**

VSUPPT

.14 (.03)**

-.09 (.09) -.05 (.14) .03 (.09) .03 (.07)


101

RACIAL COMPARISONS One might be able to make comparisons by race regarding violent offending and its predictors by simply looking to the results of the models estimated for African Americans and Whites. By comparing the random coefficient regression analyses for both racial groups, one finds that although both groups decrease in their violent offending at the same rate, African Americans had committed more violent offenses than Whites at wave one. Also, the control models for the two groups show that there is a greater gender difference in the initial status on the outcome in African Americans than in Whites. SES also displayed differential predictive value across race groups; SES was predictive in White violent offending but not for African American violent offending. Also, while victimization and conforming values were important in predicting violent offending in both races, delinquent peers, and social support were predictive of violent offending in Whites but not African Americans. The models with interaction terms also showed somewhat of a racial difference. While the interaction of victimization and association with delinquent peers was the only statistically significant (p < .05) interaction for Whites, both VDPEER and the interaction of victimization and conforming values was significant in predicting violent offending in African Americans. By comparing the models for Whites and African Americans, at first glance, one may assume that the sixth hypothesis of this study regarding the racial difference between the effect of victimization on violent offending is somewhat supported. When looking at the control models and models containing interaction terms for Whites and the same models for African Americans, one can see that victimization was found to increase the number of violent offense slightly more for African Americans than for Whites in all models. However, recall the reliability results of the previous chapter regarding victimization. It showed that the items on the scale representing victimization at time 1 were more reliable for African Americans than Whites. Therefore, the results found may be the result of a racial difference in the reliability of the scale used to measure victimization rather than an actual racial difference.

102


GENDER COMPARISONS Comparing Tables 5.7 through 5.10 may provide some information regarding the differential ability of GST constructs along with the other variables to predict violent offending for males and females. In comparing the random coefficient models for males and females, it is obvious that males had a higher initial status on violent offending than females. Also, the males had a greater amount of change in violent offending from wave to wave than females. Also, more of the variables in the model were found to be predictive of violent offending in males than in females. Only SES and delinquent peers were found to be predictive of female violent offending while victimization, race, SES, age, delinquent peers, and conforming values were all important in predicting male violent offending. The same can be said when comparing the models containing interaction terms. In each of the four models, more of the variables were important in predicting male offending than in predicting female violent offending. No interactions were significant in predicting female violent offending. However, the interaction of victimization and delinquent peers was significant (p < .05) in predicting violent offending for males even though the negative coefficient was a somewhat perplexing finding. Therefore, in comparing models across gender groups, one may at first assume that the fifth hypothesis of this study regarding the gender difference in the effect of victimization on violent offending is supported. Victimization was only significant (p < .01) in females in one instance, in the presence of the interaction of victimization and the association of delinquent peers. Victimization was found to be significant (p < .05) in every model predicting male violent offending. However, the reliability results of the previous chapter may also help to explain the gender difference found in the models predicting violent offending. The items on the scale measuring victimization are more reliable for males than females. Therefore, the difference in statistical significance of victimization for males and females may be due to a gender difference in how well the victimization scale measures victimization in males and females.

CHAPTER 6

Adolescent Victimization and Illicit Drug Use

FULL PANEL Table 6.1 gives information regarding the random coefficient regression model and the control model predicting illicit drug use in the full panel. It shows that the average score on the illicit drug use scale at time one for the full panel was .44. This increased by an average of .35 per wave. The control model showed that being males increased the average score on the illicit drug use scale at wave one by .19, holding constant the effect of all other variables, showing that females in the panel for this study had a more illicit drug use than males at the beginning of the NYS. Also, being male increased the change in illicit drug use at each wave by .11, holding constant the effect of all other variables. Also, SES was statistically significant (p < .05) in predicting the change in illicit drug use between waves as well. Table 6.1 also showed that in the control model, each year increase in age, over time, increased illicit drug use by .18. Victimization, association with delinquent peers and conforming values were also statistically significant (p < .01) in predicting illicit drug use in the full panel. Each additional incident of victimization, over time, caused an increase in the illicit drug use scale of .07, holding constant the effect of all other variables. Each unit increase on the delinquent peers scale, over time, caused a .23 increase on the illicit drug use scale, holding constant the effect of all other variables. Furthermore, over time, each unit increase on the conforming values scale was associated with a .16 decrease on the illicit drug use scale, holding constant the effect of all other variables, showing that having values supporting of conventional values can be related to decreased drug use. 103

104


Table 6.1. Random coefficient regression and control models predicting illicit drug use, full panel, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 RACE, γ02 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 RACE, γ12 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70

Control

.44 (.05)**

-.58 (.67) -.19 (.08)* -.21 (.17) .01 (.02)

.35 (.03)**

.16 (.07)* .11 (.05)* -.06 (.07) -.03 (.01)* .18 (.03)** .07 (.02)** .23 (.02)** -.03 (.02) -.16 (.02)** .03 (.02)

* p < .05 ** p < .01

Table 6.2 gives information on the models predicting illicit drug use in the full panel that contain interaction terms. In the VGOALS and VSUPPT models, it was found that females that had a higher initial status on the drug use scale than males. Also, in those two models, being male increased the rate of change from wave to wave by .11, holding constant the effect of all other variables. Also, each additional unit on the SES scale decreased the amount of change on the illicit drug use scale by .03, holding constant the effect of all other variables. Furthermore, in the VGOALS and VSUPPT models, over time, increased age, victimization and, association with delinquent peers caused increased illicit drug use in the full panel. Also, each additional


105

increase on conforming values scale caused a .16 decrease on the illicit drug use scale, holding constant the effect of all other variables. Table 6.2. Models with interaction terms predicting illicit drug use, full panel, coefficients (standard errors) VDPEER Intercept, β0 Intercept, γ00 GENDER, γ01 RACE, γ02 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 RACE, γ12 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80

VGOALS

VVALUES

VSUPPT

-.07 (.63) -.13 (.08) -.17 (.13) .01 (.02)

-.62 (.67) -.19 (.08)* -.21 (.14) .01 (.02)

-.70 (.64) -.15 (.08) -.19 (.13) .01 (.02)

-.62 (.67) -.19 (.08)* -.21 (.14) .01 (.02)

.14 (.07) .09 (.05) -.09 (.08) .03 (.01)*

.16 (.07)* .11 (.05)* -.06 (.08) .03 (.01)*

.13 (.07) .11 (.05)* -.06 (.07) -.03 (.01)*

.16 (.08)* .11 (.06)* -.06 (.08) -.03 (.01)*

.18 (.03)**

.18 (.03)**

.18 (.03)**

.18 (.03)**

.01 (.01)

.06 (.02)**

.03 (.01)

.06 (.01)*

.19 (.02)**

.23 (.02)**

.22 (.02)**

.23 (.01)**

-.03 (.02)

-.03 (.02)

-.03 (.02)

-.03 (.02)

-.16 (.02)**

.16 (.02)**

-.15 (.02)**

.16 (.01)**

.01 (.02)

.03 (.02)

.02 (.02)

.03 (.02)

.25 (.05)** -.05 (.04) -.29 (.06)** -.03 (.03)

* p < .05 ** p < .01

Table 6.2 also reveals that two of the interaction terms, VDPEER and VVALUES, were both statistically significant (p < .01) in predicting illicit drug use in the full panel. Over time, each additional

106


unit increase on the VDPEER variable is associated with a .25 increase on the illicit drug use scale, showing a combination of increased victimization and more association with delinquent peers does increase illicit drug sue. This is different from what was found earlier in predicting violent offending. This finding actually coincides with what Agnew (1992) theorized regarding victimization and moderating factors. In the model containing VDPEER, SES was found to be statistically significant (p < .05) in predicting the change in status on the illicit drug use variable. Also, in the VDPEER model, over time, each unit increase on the delinquent peers scale caused a .19 increase in the illicit drug use scale, holding constant the effect of all other variables. In the model containing VVALUES, the interaction of victimization and conforming values was found to be statistically significant (p < .01) and negative, revealing that increased victimization along with increased attitudes towards supporting conforming behavior causes a decrease in illicit drug use in the full panel, holding constant the effect of all other variables. This shows that perhaps the effect of victimization on illicit drug use is moderated by conforming values Table 6.2 shows that being male and being from a family with a lower household income each increases the change in illicit drug use for the full panel. Also, in this model, increased age, victimization and attitudes favorable towards deviant behavior each increase the amount of illicit drug use in the full panel over time.

AFRICAN AMERICANS Tables 6.3 and 6.4 give information regarding models predicting illicit drug use in the African Americans in the panel. Table 6.3 shows that the average initial status on the illicit drug use scale is .40 for African Americans. The average rate of change on the scale is .27 per wave. The control model reveals that age, victimization and association with delinquent peers each were important in predicting illicit drug use in African Americans. Each year increase in age, over time, increased illicit drug use in African Americans by .26, holding constant the effect of all other variables. Also, each additional incident of victimization, over time, caused a .11 increase on the illicit drug use scale, holding constant the effect of all other variables. Also, for African Americans,


107

the control model in Table 6.3 reveals that over time, each unit increase on the delinquent peers scale caused a .14 increase on the illicit drug use scale, holding constant the effect of all other variables. As with the full panel, Table 6.4 shows that the interaction of victimization and delinquent peers and the interaction of victimization and conforming values were found to be statistically significant (p < .01 and p < .05). Each unit increase on the VDPEER variables, over time, caused a .25 increase on the illicit drug use variable, controlling for the effect of all other variables. Also, each unit increase on the VVALUES scale caused a .32 decrease on the illicit drug use scale, holding constant the effect of all other variables. Both of these shows that the combination of more victimization and more association with delinquent peers lead to increased illicit drug use in African Americans and that the effect of victimization was moderated by the effect of conforming values. The four models predicting illicit drug use in African Americans that contained interaction terms show that increased age, increased victimization both, over time led to increased illicit drug use. Also, conforming values was statistically significant (p < .05) only in the VDEER model, while victimization was statistically significant (p< .05) in the models containing VGOALS, VVALUES, and VSUPPT.

108


Table 6.3. Random coefficient regression and control models predicting illicit drug use, African Americans, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

Control

.40 (.12)*

-3.51 (1.32)* -.02 (.22) .04 (.07)

.27 (.07)**

-.05 (.11) .20 (.12) -.01 (.03) .26 (.07)** .11 (.04)* .14 (.03)* -.06 (.04) -.05 (.03) .04 (.03)


109

Table 6.4. Models with interaction terms predicting illicit drug use, African Americans, coefficients (standard errors) Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VDPEER

VGOALS

VVALUES

VSUPPT

-2.91(1.22)* .06 (.20) .06 (.08)

-3.50 (1.31)* .02 (.22) .04 (.07)

-.06 (.11) .13 (.11) -.03 (.03)

.05 (.11) .20 (.12) .01 (.03)

-.05 (.11) .18 (.12) -.02 (.03)

-.05 (.11) .19 (.12) -.01 (.03)

.28 (.06)**

.26 (.07)**

.26 (.06)**

.26 (.07)**

.02 (.03)

.11 (.04)*

.06 (.03)*

.11 (.04)*

.11 (.03)*

.11 (.03)**

.14 (.03)**

.15 (.04)**

-.06 (.04)

-.06 (.05)

-.05 (.04)

-.06 (.05)

-.07 (.03)*

-.05 (.03)

-.06 (.03)

-.05 (.03)

.02 (.03)

.04 (.03)

.03 (.03)

.04 (.03)

-3.29 (1.39)* -3.57 (1.34)* .01 (.22) .01 (.23) .05 (.07) .04 (.07)

.25 (.05)* .01 (.05) -.32 (.10)* -.04 (.10)

110


WHITES Results about the models predicting illicit drug use in Whites can be found in Tables 6.5 and 6.6. Table 6.5 shows that the average initial status on the illicit drug use variable was .45 for Whites. White adolescents in the present study increased on the illicit drug use scale on average by .37 per wave. The control model showed that SES was important in predicting the change in White illicit drug use over time. Also, it showed that increased age, more victimization, association with delinquent peers, and having more values that do not support conforming behavior, over time, can each increase illicit drug use in White adolescents. In Table 6.6, the results for the models predicting illicit drug use in Whites that contain interaction terms are given. It shows that, like African Americans two interactions were statistically significant (p < .01), VDPEER and VVALUES. Each unit increase on the VDPEER variable caused a .25 increase in illicit drug use in Whites. over time, holding constant the effect of all other variables. This shows that the combination of more victimization and more association with delinquent peers caused more illicit drug use. Each unit increase on the VVALUES variable, over time, caused a .27 decrease on the illicit drug use scale for Whites in the panel, holding constant the effect of all other variables. This gives support to the notion that the effect of victimization may be moderated by conforming values. Table 6.6 also reveals that in all four models with interaction terms, increased age, association with delinquent peers and having values that were not approving of conforming activities were found to increase illicit drug use over time in Whites. Also, victimization was found to be statistically significant (p < .05) in models containing VGOALS, VVALUES, and VSUPPT.


111

Table 6.5. Random coefficient regression and control models predicting illicit drug use, Whites, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

Control

.45 (.06)**

-.31 (.76) -.25 (.09)* .01 (.02)

.37 (.03)**

.17 (.08)* .10 (.06) -.03 (.01)* .16 (.03)** .06 (.02)* .26 (.03)** -.02 (.03) -.17 (.02)** .02 (.02)

112


Table 6.6. Models with interaction terms predicting illicit drug use, Whites, coefficients (standard errors) Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VDPEER

VGOALS

VVALUES

VSUPPT

.07 (.76) -.19 (.08)* .01 (.02)

-.37 (.76) -.25 (.08)* .01 (.02)

.45 (.72) .20 (.08)* .01 (.02)

-.33 (.75) .25 (.09)* -.01 (.02)

.13 (.08) .09 (.05) -.02 (.01)

.16 (.08)* .10 (.06) .02 (.01)*

.14 (.08) .09 (.06) -.02 (.01)

.16 (.08)* .10 (.06) -.02 (.01)*

.17 (.03)**

.16 (.03)**

.17 (.03)**

.16 (.03)**

.01 (.01)

.06 (.02)*

.03 (.01)*

.06 (.02)*

.23 (.02)**

.26 (.03)**

.24 (.03)**

.26 (.03)**

-.02 (.03)

.01 (.02)

-.02 (.02)

-.01 (.03)

-.17 (.02)**

.17 (.02)**

.16 (.02)**

.17 (.02)**

.01 (.02)

.02 (.02)

.02 (.02)

.02 (.02)

.25 (.06)** -.06 (.04) -.27 (.06)** -.02 (.05)


113

MALES Table 6.7 reveals that the average initial status on the illicit drug use scale for the males in the panel was .58. Males increased on the scale an average of .41 per wave. The control model in Table 6.7 showed that being older, having increased victimization, more association with delinquent peers, and possessing values that were less supportive of conforming behavior each, over time, contributed to an increase on the illicit drug use scale for males. Table 6.8 shows that, as with the full panel and both racial groups, two interactions were found to be statistically significant in predicting male illicit drug use, VDPEER and VGOALS. This table shows that over time, for each unit increase on VDPEER, males had an increase of .26 on the illicit drug use scale, holding constant the effect of all other variables. This shows that increased victimization and more association with delinquent peers caused an increase in illicit drug use. Also, each unit increase on the VVALUES variable, over time, caused a .27 decrease on the illicit drug scale in males, holding constant the effect of all other variables, showing that the effect of victimization was moderated by conforming values. In Table 6.8, it also shows that in all interaction models increased age, more association with delinquent peers, and having values disapproving of conforming values, over time, each caused an increase in illicit drug use in males. In all of the models in Table 6.8 except the model containing VDPEER, increased victimization, over time, was found to increase male illicit drug use.

114


Table 6.7. Random coefficient regression and control models predicting illicit drug use, males, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

.58 (.08)**

Control -1.68 (1.06) -.17 (.22) -.01 (.04)

.41 (.05)** .17 (.11) -.02 (.12) -.02 (.02) .24 (.04)** .07 (.02)** .24 (.03)** -.04 (.03) -.16 (.03)** .04 (.03)


115

Table 6.8. Models with interaction terms predicting illicit drug use, males, coefficients (standard errors) VDPEER Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VGOALS

VVALUES

VSUPPT

-.76 (.98) -.11 (.20) .01 (.04)

-1.75 (1.04) -.17 (.22) -.01 (.04)

-1.82 (1.02) -.15 (.21) .01 (.03)

-1.66 (1.03) .17 (.22) .01 (.04)

.13 (.11) -.07 (.11) -.02 (.02)

.17 (.11) -.02 (.12) -.02 (.02)

.15 (.11) -.02 (.12) -.02 (.02)

.17 (.11) -.02 (.12) -.02 (.02)

.18 (.04)** -.01 (.02) .18 (.03)**

.24 (.04)**

.24 (.04)**

.24 (.04)**

.07 (.02)*

.03 (.02)*

.07 (.02)*

.24 (.03)**

.22 (.03)**

.24 (.03)**

-.04 (.03)

-.03 (.03)

-.03 (.03)

-.04 (.03)

-.17 (.03)**

-.16 (.03)**

-.15 (.02)**

-.16 (.03)**

.02 (.03)

.04 (.03)

.03 (.03)

.03 (.03)

.26 (.05)** -.03 (.05) -.27 (.07)** .01 (.05)

116


FEMALES Tables 6.9 and 6.10 give information about the results of models predicting illicit drug use in the females in the panel for the present study. Table 6.9 shows that the average initial status on the drug use scale is .29. Females increase on the scale, on average, by .29 per wave. The control model, it is shown that each additional increase on the SES scale caused a decrease in the change on the illicit drug use scale, holding constant all other variables. It also showed that increased age, more victimization and having more values that were disapproving of conforming behavior, over time, contributed to increased illicit drug use in females. Table 6.10 gives the results of models predicting female illicit drug use with interaction terms. It shows that, like the full panel, two of the VDPEER and VVALUES were each found to be important in predicting female illicit drug use over time. They show that the combination of increased victimization and increased association with delinquent peers, over time, caused an increase in female illicit drug use and that the effect of victimization was moderated by the effect of conforming values. Table 6.10 shows that in all four of the variables containing interaction terms, SES was statistically significant (p< .05) in predicting WAVE. Also, increased age, increased victimization, and having more values that are not supportive of conforming behavior, over time increased the illicit drug use in the females in the panel for this study.


117

Table 6.9. Random coefficient regression and control models predicting illicit drug use, females, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

Control

.29 (.05)**

.18 (.78) -.26 (.15) .02 (.02)

.29 (.04)**

.28 (.08)* -.12 (.08) -.03 (.01)* .11 (.03)** .05 (.03) .22 (.03)** -.02 (.03) -.14 (.03)** .01 (.02)

118


Table 6.10. Models with interaction terms predicting illicit drug use, females, coefficients (standard errors) Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β6 Intercept, γ60 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VDPEER

VGOALS

VVALUES

VSUPPT

.07 (.81) -.27 (.15) .02 (.02)

.24 (.79) -.25 (.14) .02 (.02)

.15 (.75) -.25 (.15) .02 (.02)

.44 (.81) -.28 (.15) .02 (.02)

.23 (.08)** -.09 (.08) -.03 (.01)*

.28 (.08)* -.13 (.08) -.04 (.01)*

.24 (.08)* -.11 (.08) -.03 (.01)*

.27 (.08)* -.11 (.08) -.03 (.01)*

.11 (.03)**

.11 (.03)**

.12 (.03)**

.11 (.03)**

.02 (.02)

.05 (.02)

.04 (.02)

.03 (.03)

.22 (.03)**

.22 (.03)**

.22 (.03)**

.22 (.03)**

-.02 (.03)

-.02 (.03)

-.02 (.03)

-.02 (.03)

-.14 (.02)**

-.14 (.03)**

-.14 (.02)**

-.14 (.03)**

.01 (.02)

.01 (.02)

.01 (.02)

.01 (.02)

.25 (.10)* -.09 (.09) -.31 (.08)** -.15 (.08)


119

RACIAL COMPARISONS From the results of the models predicting illicit drug use for Whites and African Americans, one can attempt to make some racial comparisons. By comparing Tables 6.3 and 6.5, one finds that at wave one, on average, Whites had somewhat more illicit drug use than Blacks and that illicit drug use in Whites increased at a faster rate than in African Americans. Also, the control models for the two groups show that gender was important in determining the initial status for Whites, whereas it was not significant in determining initial status for African Americans. Also, age, victimization and delinquent peers was significant (p < .05) in predicting illicit drug use in African Americans as well as Whites. Conforming values was important in predicting White illicit drugs use but not in predicting African American illicit drug use. Victimization in the White control model had a lower coefficient than in the African American control model. The models with interaction terms also seemed to display somewhat of a racial difference. In each of these models for Whites, the coefficient estimated for victimization was lower than those in corresponding models for African Americans. For example, the coefficient for victimization in the VDPEER model for Whites was lower than in the VDPEER model for African Americans. Also, while for both racial groups, both the interaction of victimization and delinquent peers and the interaction of victimization and conforming values were significant (p < .05 and p < .01, respectively), the coefficient for VVALUES for Whites was higher than that for African Americans. By comparing models for Whites and African Americans, one may assume that victimization may cause a greater increase in illicit drug use in African Americans than Whites. This may seem to not give any support for the seventh hypothesis of this study. However, recall that the reliability results in chapter 4 revealed that the scale that measured victimization at wave one was more reliable for African Americans than Whites. This may have had some effect on the coefficients estimated for VICT. Therefore, the difference in the estimates may be the result of racial differences in the reliability of the scale rather than an actual racial difference in the effect of victimization.

120


GENDER COMPARISONS By comparing tables that contain the results of models predicting illicit drug use for males and females, one may gather information on gender differences. By looking at the random coefficient regression models for both gender groups, it is shown that males, on average, started the NYS with more illicit drug use than females. Males, on average, also increased in their illicit drug use at a greater pace than that of females. The control models for males and females show that SES was statistically significant (p < .05) in predicting WAVE but was not significant for males. Age, delinquent peers and conforming values were all predictive of illicit drug use for both gender groups in the control models. However, victimization was statistically significant (p < .01) in predicting illicit drug use in the control model for males but not for females. In comparing the models containing interaction terms for both gender groups, one finds that the interaction of victimization and delinquent peers and the interaction of victimization conforming values were significant in predicting both male and female illicit drug use. Also, victimization was not found to be significant in any of the models with interactions terms predicting female illicit drug use. However, it was found to be predictive of male illicit drug use in three of the interaction models. From this, one may conclude that victimization was more significant in predicting illicit drug use in males than in females, thereby not giving any support to the fourth hypothesis of this study. However, this may not be the case. Remember that in chapter 4, the reliability of the victimization scale at wave one was higher for males than females. Low reliability may affect the coefficients in the model. Therefore, the difference in the effect of victimization on male and female illicit drug use may be due to differences in the reliability rather than an actual gender difference.

CHAPTER 7

Adolescent Victimization and Nonviolent, Non-Drug Offending

FULL PANEL Tables 7.1 and 7.2 show the results of the models predicting nonviolent, non-drug offending for the full sample. In Table 7.1, the results of the random coefficient regression and control models are given. From the random coefficient model, one can calculate that the average number of nonviolent, non-drug offenses committed by the full panel in this study was 2.66 (e.98). The number of nonviolent, non-drug offenses decreased on average by about 4% ((e-.04 – 1) * 100) per wave for the full sample. The control model revealed that being male multiplied the number of nonviolent, non-drug offenses by a factor of 2.22 (e.80) and that males had 123% ((e.80 - 1)* 100) more nonviolent, non-drug offenses than females, controlling for the effect of all other variables. Also, the time varying covariates, age, victimization, association with delinquent peers, goals, conforming values, and social support each were found to be predictive of nonviolent, non-drug offending in the full panel. Over time, each additional year in age multiplied the number of nonviolent, non-drug offenses by a factor of 1.09 and increased the number of nonviolent, non-drug offenses by 9%, controlling for the effect of all other variables. Over time, each additional incident of victimization multiplied the number of nonviolent, non-drug offenses in the full panel by a factor of 1.05 and increased the number of nonviolent, non-drug offenses by 5%, controlling for the effect of all other variables. Also, each unit increase on the association with delinquent peers scale, over time, multiplied the number of nonviolent, non-drug offenses by a factor of 1.13, and increased the number of 121

122


nonviolent, non-drug offenses by 13%, holding constant the effect of all other variables. After controlling for the effect of all other variables, over time, each unit increase on the goals scale decreased the number of nonviolent, non-drug offenses by about 10%. Each unit increase on the conforming values scale (having values that were more approving of conforming behavior), over time, decreased the amount of nonviolent, non-drug offenses by about 9%, controlling for the effect of all other variables. Over time, each unit increase on the social support scale decreased the number of nonviolent, non-drug offenses by about 6%, controlling for the effect of all other variables. Table 7.2 gives the results of the models that contained interaction terms. They reveal that unlike in the control model, age, social support, and goals are not statistically significant (p > .05) in predicting nonviolent, non-drug offending once the interaction terms were included in the model. However, these models do have some similarities with the control model. In all four of these models, it is shown that, controlling for all other variables, being male increased the amount of nonviolent, non-drug offending at wave one. Also, increased victimization, an increased association with delinquent peers, and having values that were more approving of conforming behavior, were each also found to increase the number of nonviolent, non-drug offenses, controlling for all other variables. However, as shown in Table 7.2, none of the four interactions were found to be statistically significant in predicting the number of nonviolent, non-drug offending in the full panel.


123

Table 7.1. Random coefficient regression and control models predicting nonviolent, non-drug offending, full panel, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 RACE, γ02 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 RACE, γ12 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

.98 (.09)**

-.04 (.04)

Control 1.56 (1.21)* .80 (.19)** -.12 (.22) -.01 (.04) -.19 (.17) -.08 (.12) -.04 (.19) -.01 (.02) .09 (.05)* .05 (.02)** .12 (.12)** -.11 (.06)** -.09 (.03)** -.06 (.05)**

124


Table 7.2. Models with interaction terms predicting nonviolent, non-drug offending, full panel, coefficients (standard errors) VDPEER Intercept, β0 Intercept, γ00 GENDER, γ01 RACE, γ02 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 RACE, γ12 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80

VGOALS

VVALUES

VSUPPT

1.05 (1.26) 1.30 (1.21) .75 (.18)** .81 (.18)** -.11 (.22) -.12 (.22) -.01 (.04) -.01 (.04)

1.47 (1.22) .82 (.19)** -.12 (.22) -.01 (.04)

1.20 (1.06) .80 (.19)** -.09 (.22) -.01 (.04)

-.19 (.17) -.07 (.12) .05 (.19) -.01 (.02)

-.19 (.17) -.08 (.12) .06 (.19) -.01 (.02)

-.19 (.17) -.09 (.12) .04 (.19) -.01 (.02)

-.20 (.16) -.07 (.12) .04 (.19) -.01 (.02)

.09 (.05)

.09 (.05)

.09 (.05)

.09 (.05)

.07 (.02)*

.05 (.02)*

.04 (.03)

.04 (.03)

.13 (.02)**

.12 (.02)**

.12 (.02)**

.12 (.02)**

-.11 (.06)

-.09 (.07)

-.11 (.06)

-.12 (.06)

-.08 (.03)*

-.09 (.03)*

-.09 (.03)*

-.09 (.03)*

-.05 (.05)

-.06 (.05)

-.06 (.04)

-.03 (.05)

-.03 (.02) -.04 (.04) -.01 (.04) -.04 (.07)

* p < .05 ** p < .01

AFRICAN AMERICANS Tables 7.3 and 7.4 gives the results of models predicting nonviolent, non-drug offending that is estimated for African Americans. From the random coefficient regression model, one can determine that the


125

average initial number of nonviolent, non-drug offenses committed by the African Americans in the full panel was 2.66 (e.98), same as with the full panel. Also, the number of nonviolent, non-drug offenses decreased on average by about 6% ((e-.06 - 1)* 100) per wave. The control model reveals that only the intercept in the equation for WAVE is statistically significant (p < .05) in predicting nonviolent, non-drug offending in African Americans. Controlling for all of the other variables in the model, it showed that for each wave, the number of nonviolent, non-drug offense decreased by about 50% per wave. All other variables in the control model were not statistically significant (p > .05). The same result was shown in the Table 7.4, in the models containing interaction terms. In all four models in the table, only the intercept in the equation for β1 was found to be statistically significant (p < .05) in predicting nonviolent non-drug offending. None of the other variables were found to be statistically significant, including the interaction terms and victimization.

126


Table 7.3. Random coefficient regression and control models predicting nonviolent, non-drug offending, African Americans, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

.98 (.20)**

-.06 (.10)*

Control 1.46 (5.62) .41 (.50) -.07 (.10) -.70 (.23)* .28 (.26) .08 (.08) .18 (.18) .06 (.12) .02 (.06) .07 (.10) -.10 (.10) -.10 (.13)


127

Table 7.4. Models with interaction terms predicting nonviolent, non-drug offending, African Americans, coefficients (standard errors) Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05

VDPEER

VGOALS

VVALUES

VSUPPT

1.01 (5.30) .44 (.44) -.08 (.10)

-.97 (5.05) .40 (.48) -.09 (.11)

-.56 (6.11) .16 (.70) -.09 (.15)

-.68 (5.01) .38 (.59) .10 (.12)

-.69 (.22)* .27 (.24) .11 (.07)

-.67 (.22)* .30 (.25) .08 (.07)

-.74 (.27)* .41 (.31) .08 (.09)

.83 (.25)* .25 (.29) .08 (.08)

.16 (.16)

.18 (.17)

.15 (.22)

.22 (.19)

.10 (.08)

.10 (.08)

.04 (.10)

.02 (.11)

.06 (.05)

.05 (.04)

.03 (.05)

.04 (.05)

.10 (.10)

.12 (.12)

.14 (.12)

.06 (.10)

-.09 (.11)

-.07 (.11)

-.01 (.12)

.10 (.11)

-.13 (.11)

-.05 (.09)

-.09 (.12)

.01 (.11)

-.01 (.15) -.05 (.24) -.19 (.13) -.17 (.32)

128


WHITES Tables 7.5 and 7.6 give information regarding the growth curve models estimated for Whites in the panel in this study. From Table 7.5, it can be calculated that the average number of nonviolent, non-drug offenses committed by Whites in the panel is 2.66 (e.98). This number decreased on average by about 4% ((e-.04 - 1)* 100) per wave. In the control model, it is shown that White males had 129% more nonviolent, nondrug offenses at wave one than White females, controlling for the effect of all other variables. From the control model one can also calculate that, over time, each additional incident of victimization increased the number of nonviolent, non-drug offenses by about 4% ((e-.04 - 1)* 100), controlling for the effect of all other variables. Also, each unit increase on the delinquent peers scale, over time, increased the number of nonviolent non-drug offenses by about 15%, holding constant the effect of all other variables. Each unit increase on the VALUES variable increased the number of nonviolent/non-drug offenses in Whites by about 8%, controlling for the effect of all other variables. The models in Table 7.6 reveal the same results gained in the control model. They all show that being male increased the number of nonviolent, non-drug offenses by Whites at wave one. The four models also show that increased victimization more association with delinquent peers and having values more favorable towards deviant behavior each increased the amount of nonviolent, non-drug offending in Whites. Consistent with the full panel and African Americans, however, none of the interactions were found to be statistically significant (p < .05).

Adolescent Victimization and Nonviolent, Non-Drug Offending Table 7.5. Random coefficient regression and control models predicting nonviolent, non-drug offending, Whites, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

.98 (.10)**

-.04 (.09)*

Control 1.27 (1.13) .83 (.20)** -.01 (.04) -.11 (.18) -.12 (.12) -.01 (.02) .08 (.06) .04 (.02)* .14 (.02)** -.14 (.07) -.08 (.03)* -.05 (.05)

129

130


Table 7.6. Models with interaction terms predicting nonviolent, non-drug offending, Whites, coefficients (standard errors) Intercept, β0 Intercept, γ00 GENDER, γ01 SES, γ03 WAVE, β1 Intercept, γ10 GENDER, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VDPEER

VGOALS

VVALUES

VSUPPT

.80 (1.19) .78 (.20)** -.01 (.04)

1.18 (1.13) .84 (.20)** -.01 (.04)

1.35 (1.12) .80 (.20)** -.01 (.04)

1.08 (1.12) .83 (.20)** -.01 (.04)

-.11 (.18) -.10 (.12) -.01 (.02)

-.11 (.18) -.12 (.12) -.01 (.02)

-.11 (.18) -.11 (.12) -.01 (.02)

-.12 (.18) -.11 (.12) -.01 (.02)

.07 (.06)

.08 (.06)

.08 (.06)

.08 (.06)

.07 (.02)*

.04 (.02)*

.05 (.02)*

.04 (.02)

.15 (.02)**

.14 (.02)**

.14 (.02)**

.14 (.02)**

-.14 (.08)

-.13 (.08)

-.14 (.08)

-.07 (.03)*

-.08 (.03)*

-.09 (.03)*

-.04 (.05)

-.05 (.05)

-.04 (.04)

-.14 (.08) .09 (.03)* -.03 (.05)

-.03 (.02) -.01 (.04) .03 (.02) -.02 (.04)


131

MALES Results of the growth curve models estimated for males are displayed in Table 7.7. As can be seen, the average initial number of nonviolent, non-drug offenses committed by males in the panel for the study was 4.22 (e1.44). On average, this number decreased by about 11% per wave. For males, age, victimization, association with delinquent peers, goals, and conforming values each were found to be predictive of nonviolent, non-drug offending. More specifically, over time, with each additional year in age, the number of nonviolent non-drug offenses is multiplied by a factor of 1.13, and the number of nonviolent non-drug offenses by about 13%, controlling for the effect of all other variables. Each additional incident of victimization, over time, multiplied the number of nonviolent, non-drug offenses by a factor of 1.04 (e.04) and increases the number by about 4%, controlling for the effect of all other variables. Also, over time, each unit increase on the delinquent peers scale was associated with about a 12% increase in the number of nonviolent, non-drug offenses in males, holding constant the effect of all other variables. Also, over time, each unit increase on the goals scale caused a 12% decrease in the number of nonviolent non-drug offenses in males, controlling for the effect of all other variables. Each unit increase on the VALUES scale, over time, decreased the number of nonviolent, non-drug offenses by about 8%, controlling for the effect of all other variables. Table 7.8 gives information regarding the models predicting nonviolent, non-drug offending estimated for the Whites in the current study. Consistent with all models predicting nonviolent, non-drug offending previously mentioned, none of the interactions were found to be statistically significant (p < .05).

132


Table 7.7. Random coefficient regression and control models predicting nonviolent, non-drug offending, males, coefficients (standard errors) Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 * p < .05 ** p < .01

Random Coefficient

Control

1.44 (.10)**

2.09 (1.30) -.21 (.25) -.02 (.04)

-.11 (.04)

-.30 (.12)* .10 (.14) -.01 (.02) .12 (.05)* .04 (.02)* .11 (.02)** -.13 (.08)* -.08 (.03)* -.06 (.05)


133

Table 7.8. Models with interaction terms predicting nonviolent, non-drug offending, males, coefficients (standard errors) Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VDPEER

VGOALS

VVALUES

VSUPPT

1.65 (1.37) -.20 (.26) -.02 (.04)

1.75 (1.34) -.23 (.26) -.02 (.04)

2.02 (1.31) -.22 (.25) -.02 (.04)

1.34 (1.14) -.18 (.25) -.02 (.04)

-.29 (.12)* .11 (.15) -.01 (.02)

-.31 (.12)* .13 (.15) -.01 (.01)

-.31 (.12)* .11 (.14) -.01 (.02)

-.30 (.12)* .09 (.14) -.01 (.02)

.12 (.05)*

.12 (.05)*

.12 (.05)*

.12 (.05)*

.06 (.02)*

.04(.02)*

.04(.02)*

.04(.02)*

.13 (.02)**

.12 (.02)**

.11 (.02)**

.12 (.02)**

-.13 (.07)*

-.09 (.08)*

-.13 (.06)*

-.14 (.07)*

-.08 (.03)*

-.08 (.03)*

-.08 (.03)*

-.08 (.03)*

-.05 (.05)

-.06 (.05)

-.06 (.05)

-.05 (.05)

-.02 (.02) -.05 (.04) -.01 (.04) -.03 (.06)

134


FEMALES From Table 7.9, one can calculate that the average initial number of nonviolent, non-drug offenses committed by the females in the panel for this study is 1.01 (e.01). This number increases on average by about 25% ((e.22 – 1) * 100) per wave. The control model in Table 7.9 reveals that victimization, association with delinquent peers and conforming values all were predictive of nonviolent, non-drug offending in males. More specifically, based on the information from the control model, over time, each additional incident of victimization multiplied the number of nonviolent, non-drug offenses in females by a factor of 1.08, and increased the number by 8%, controlling for the effect of all other variables. Also, over time, each unit increase on the delinquent peers scale multiplied the number of nonviolent, non-drug offenses committed by females by a factor of 1.13 and increased the number by about 13%, holding constant the effect of all other variables. Furthermore, over time, each unit increase on the conforming values scale decreased the number of nonviolent, non-drug offenses in females by about 10%, controlling for the effect of all other variables. Table 7.10 provides results for the models predicting nonviolent, non-drug offending for females that contain interaction terms. Again, none of the interactions were found to be statistically significant (p > .05) in predicting nonviolent, non-drug offending in females. Consistent with the control model, however, victimization, association with delinquent peers, and conforming values all were statistically significant (p < .05) of nonviolent, non-drug offending in females.

Adolescent Victimization and Nonviolent, Non-Drug Offending Table 7.9. Random coefficient regression and control models predicting nonviolent, non-drug offending, females, coefficients (standard errors) Random Coefficient Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 * p < .05

.01 (.13)

.22 (.09)*

Control .26 (1.76) .21 (.43) .01 (.08) -.21 (.24) .06 (.39) .01 (.04) .08 (.13) .08 (.03)* .12 (.04)* -.10 (.11)* -.10 (.04)* .02 (.05)

135

136


Table 7.10. Models with interaction terms predicting nonviolent, non-drug offending, females, coefficients (standard errors) Intercept, β0 Intercept, γ00 RACE, γ01 SES, γ03 WAVE, β1 Intercept, γ10 RACE, γ11 SES, γ13 AGE, β2 Intercept, γ20 VICT, β3 Intercept, γ30 DPEER, β4 Intercept, γ40 GOALS, β5 Intercept, γ50 VALUES, β7 Intercept, γ70 SUPPT, β7 Intercept, γ70 VDPEER, β8 Intercept, γ80 VGOALS, β8 Intercept, γ80 VVALUES, β8 Intercept, γ80 VSUPPT, β8 Intercept, γ80 * p < .05 ** p < .01

VDPEER

VGOALS

VVALUES

VSUPPT

.25 (1.72) .20 (.41) .02 (.08)

.22 (1.78) .23 (.45) .01 (.08)

.36 (1.78) .20 (.44) .01 (.08)

.27 (1.78) .21 (.44) .01 (.08)

-.14 (.23) .05 (.38) -.01 (.04)

-.19 (.23) .05 (.38) -.01 (.04)

-.21 (.24) .07 (.40) .01 (.04)

-.21 (.24) .06 (.39) .01 (.04)

.09 (.13)

.08 (.13)

.08 (.13)

.08 (.13)

.12 (.06)*

.08 (.10)*

.08 (.04)*

.08 (.05)*

.13 (.03)*

.12 (.04)*

.12 (.04)*

.12 (.04)*

-.12 (.11)

-.10 (.12)

-.10 (.11)

-.10 (.11)

-.12 (.04)*

-.10 (.03)*

-.10 (.04)*

-.10 (.04)*

.04 (.05)

.03 (.05)

.02 (.05)

.02 (.05)

-.13 (.11) .01 (.10) .01 (.05) -.03 (.05)


137

RACIAL COMPARISONS By comparing the random coefficient regression models predicting nonviolent, non-drug offending estimated for both racial groups, it appears that both African Americans and Whites start the panel with approximately the same number of nonviolent, non-drug offenses. Moreover, both groups decrease their offending at nearly the same rate over time. However, comparing control models by race does, at first glance, reveal some differences. The control models reveal that for Whites, victimization, association with delinquent peers and conforming values all effect nonviolent, non-drug offending over time. However, none of these predictors except the growth rate were found to be statistically significant (p > .05) in predicting nonviolent, non-drug offending in African Americans. This difference still seemed to be true in comparing the models containing interaction terms by race. While for both Whites and African Americans, none of the interaction terms were found to be statistically significant in predicting nonviolent, non-drug offending, only the growth rate proved to be statistically significant (p < .05) for African Americans while victimization, association with delinquent peers, conforming values were all found to be predictive of nonviolent, non-drug offending in Whites. While a racial difference may appear to exist by comparing models, recall that there is a smaller number of African Americans than Whites. This difference in sample sizes might affect the statistical significance found in the models. Therefore, the racial difference may be a reflection of the differences in sample size than an actual racial difference.

GENDER COMPARISONS In comparing models for male and females that predict nonviolent/nondrug offending, males were found to have a higher average number on the outcome at wave one than females. Also, the random coefficient regression models show that males were found to decrease, on average, in their nonviolent non-drug offending over time while females increased on average. This is consistent with the descriptive statistics that were presented earlier. Recall that Table 4.2 revealed that after a slight increase at wave two, males decreased in their average number of

138


nonviolent, non-drug offenses. Conversely, for females, after a slight decrease at wave two, the average number of nonviolent, non-drug offenses increased. Also, for both males and females, the control models reveal that victimization, delinquent peers, societal goals, and conforming values were all predictive of nonviolent, non-drug offending. Also, in the control model estimated for males, age was also found to be significant (p < .05) in predicting nonviolent, non-drug offending. In looking at models in Tables 7.8 and 7.10, none of the interactions were found to be statistically significant (p < .05) in predicting nonviolent, non-drug offending for either males or females. Since victimization increased the number of nonviolent, non-drug offenses over time for females more than males, it appears that victimization may be a slightly more significant predictor of these types of offenses for females compared to males. This finding seems to be unusual since both the scale measuring victimization and nonviolent/non-drug offending at wave one were found to be more reliable for males than females.

CHAPTER 8

Can GST Explain the Relationship Between Adolescent Victimization and Delinquency?

Table 8.1 summarizes all of the results of this study and underscores the significance of victimization in explaining all three types of delinquency examined in this research by all race and gender subgroups. The control model is presented along with the interaction term models as well. Table 8.1. Significance of victimization in explaining delinquency for all models

VIOLENT Full Black White Male Female DRUGS Full Black White Male Female NONVIO Full Black White Male Female * p < .05

Control VDPEERS VVALUES VGOALS VSUPPT Model Model Model Model Model VICT VICT VICT VICT VICT * ** * * * * ** * * * * ** * * * ** ** ** * ** ** ** ** * * * * * * * * * ** * * * ** * * *

* * *

**p < .01

139

* * *

* * *

* *

140


RETURNING TO THE RESEARCH QUESTIONS AND HYPOTHESES 1. Can Agnew’s general strain theory (GST) explain the relationship between adolescent victimization and delinquency? As mentioned earlier, this study attempted to answer the question: Can Agnew’s general strain theory (GST) explain the relationship between adolescent victimization and delinquency? Based on the results of this study, the answer is a qualified yes. This study has shown that the main premise of GST, increased sources of strain can lead to increased delinquent behavior, has been supported by this study. Increased victimization, as a source of strain, was found to predict increased amounts of violent offending, illicit drug use and nonviolent non-drug offending in the panel. This positive linear relationship between victimization and delinquency is similar to other empirical support found previously for GST (Agnew et al. 2002; Agnew and White 1992; Katz 2000; Mazerolle 1998; Mazerolle et al. 2000; Mazerolle and Maahs 2000; Piquero and Sealock 2004). However, this study’s support of GST is conditional when one considers the results of the interaction terms that represent the conditioning of strain. In a few instances, the result of the interactions ran similar to what had been predicted in Agnew’s (1992) general strain theory. However, other results were contrary to what Agnew has argued. Even though the answer to the first research question can be answered with an affirmative response based on the results of the present study, it is important to be careful in concluding whether victimization is actually conditioned by the various factors that Agnew (1992) suggests, including delinquent peers, values, goals, and social support. This conditioning may depend on the type of delinquent behavior involved. None of the interactions examined here were found to predict nonviolent non-drug offending for any subgroup of the panel while the effect of victimization was conditioned by other factors when predicting violent offending and illicit drug use.

The Relationship Between Victimization and Delinquency

141

2. Can GST explain the relationship between adolescent victimization and violent offending? (Testing hypothesis one) When looking at violent offending, the results of this study generally supported the basic premise of GST; that increased strain would lead to increased violent delinquency. This study found that increased victimization was related to increased violent offending in most models. This is similar to other literature, though not grounded in theory; that looked at the effect of victimization on violent behavior (Cleary 2000; Ireland et al. 2002; Rivera and Widom 1990; Shaffer and Ruback 2002; Widom 1989c). This provides an affirmative answer to the second research question and provides some support to the first hypothesis presented in this study. However, as alluded to earlier, testing to see how victimization was affected by the various conditioning factors produced some results that actually run counter to what was originally expected. Specifically, this study showed that the combination of increased victimization and more association with delinquent peers produced a lower level of violent offending in all models except the models for the females in the panel. Another explanation could be that a resilience factor was present in environments with delinquent peers that was not included in this study. Yet another possibility is that there could have been collinearity between victimization and association with delinquent peers that may have caused the unusual finding. 3. Can GST explain the relationship between adolescent victimization and nonviolent/ non-drug offending? (Testing hypothesis three) The third research question in this study concerning GST and nonviolent, non-drug offending can also be answered with a qualified affirmative response, providing hypothesis three with some support. Increased victimization was found to predict increased nonviolent, nondrug offending in the panel used for the present study. This is similar to previous research literature done on the effect of victimization on various types of nonviolent non-drug offending (Heck and Walsh 2000; Herrera and McCloskey 2003; Menard 2002; Stouthamer-Loeber et al. 2001; Widom and White 1997). An interesting finding relating to this outcome was that in predicting nonviolent, non-drug offending, none of the interaction

142


terms proved to be predictive of the outcome. This means that the effect of victimization on the nonviolent and non-drug related offending was not affected by any of the conditioning factors. Thus, according to these results, the effect of victimization on nonviolent, non-drug offending is simply not conditioned either by conforming values, societal goals, delinquent peers or social support. Mazerolle and Maahs (2000) provided another argument that may shed light on this finding. They stated that using multiplicative interaction terms, as were used in this study, in a multiple regression framework might present some difficulty in estimating coefficients for interaction terms that are statistically significant. Statistically, this is because researchers often include a large number of other variables along with the interaction terms. The sheer number of these terms may make it difficult to produce statistically significant terms. Because the statistical technique used in the present study is based on some of the principles of multiple regression and other variables were included when the interaction terms were tested, their argument might explain the lack of statistically significant interaction terms here. 4. Can GST explain the relationship between adolescent victimization and illicit drug use? (Testing hypothesis two) This study also discovered that victimization was associated with an increase in illicit drug use in the full panel in this study providing support for the second hypothesis of this study. This result is also similar to the research literature, though not grounded in theory, regarding the effect of victimization on illicit drug use (Dembo et al. 1987; Kilpatrick et al. 2000; Menard 2002). For example, Perez (2000) found that physical and sexual victimization both increased adolescent illicit drug use. Further analysis of the results reveals that in predicting illicit drug use in adolescents, the effect of victimization was affected by several variables, as suggested by Agnew (1992). For example, it was found that for the full panel, the effect of victimization was moderated by conforming values for the full panel. In other words, having values that were more approving of conforming behavior along with increased victimization led to a decrease in illicit drug use, showing that conforming values moderated the effect of victimization. On the other hand, association with delinquent peers exacerbated the effect of victimization. The combination of increased victimization along with


143

more association with delinquent peers was found to increase the amount of illicit drug use in the panel. 5. How do the relationships posited in questions 2 through 4 vary according to race? (Testing hypotheses six and seven) To answer the fifth research question presented in the present study, one must consider each of the outcomes separately. In terms of violent offending, increased victimization was found to be an important predictor for both racial groups in the panel. However, victimization was found to increase violent offending slightly more over time for African Americans than Whites in the panel. This lends some support to the sixth hypothesis regarding racial differences in the effect of victimization on violent offending. This conclusion coincides with what Eitle and Turner (2003) hypothesized and found when applying GST to the relationship between stress and crime across racial groups. It also coincides with other empirical research not grounded in criminological theory (Rivera and Widom 1990; Widom 1989a). Kruttschnitt and Dornfeld (1991) and Maxfield and Widom (1996) discovered that victimization increased violent offending for African Americans but not for Whites. However, most the previous studies looking at racial differences in the victimization/violent offending relationship considered the effect of childhood not adolescent victimization. Even though this conclusion seems to support the sixth hypothesis in this study and previous literature on racial differences, this conclusion must be viewed with caution. The victimization scale was more reliable for African Americans than Whites. This could actually affect the coefficients estimated. Thus, this difference could be due to differences in reliability rather than an actual racial difference. In looking at the effect of victimization on illicit drug use, the present study found that victimization had a greater effect on illicit drug use in African Americans than Whites. Even though Whites had more illicit drug use at the first wave and increased in their illicit drug use faster than African Americans, victimization seemed to cause a greater increase in illicit drug use in African Americans than Whites. This does not provide support to the seventh hypothesis in the present study. It also is the opposite of other cross-sectional research on the effect of victimization and illicit drug use in adolescents. Harrell (2001) discovered in a cross-sectional study that victimization did increase

144


substance use in Whites more so than in African Americans. A reason for this study’s finding could be due to the racial difference in reliability of the victimization scale as mentioned earlier. This could have seriously affected the results regarding victimization and illicit drug use. Also, this study found that victimization increased nonviolent, nondrug offending more in Whites than African Americans. This finding could have been affected by the racial difference in the sample sizes. There were more Whites than African Americans in the study. This could have had serious effects on finding levels of statistical significance in the African Americans in the panel. Therefore, this finding could be the result of unequal sample sizes rather than an actual racial difference. 6. How do the relationships posited in questions 2 through 4 vary according to gender? (Testing hypotheses four and five) At first glance, this study seemed to have support for the fifth hypothesis that stated that victimization would have a greater effect in predicting violent offending in males than females. This is similar to some of the previous empirical and theoretical research. For example, Broidy and Agnew (1997) argued that males are more likely to experience strain that stems from interpersonal conflict that, in turn, will lead to more violent behavior than females. All components of the victimization variable used in this study were examples of interpersonal conflict. The males in this study did report more victimization than the females in this study. Victimization was found to be predictive of violent offending for males but not for females. This shows some support for Broidy and Agnew’s arguments regarding gender and crime. However, when one factors in reliability, this conclusion is called into question. The scale measuring victimization at wave one was more reliable for males than females. This could have had serious effects on how well victimization predicts violent offending in the two groups. Therefore, the gender difference in the effect of victimization on violent offending found in this study could have been due to gender differences in reliability of the scale measuring victimization. In looking at how the victimization/illicit drug use relationship differed according to gender, some of the results were found to be different than what was expected. It was stated in the fourth hypothesis


145

that victimization would increase illicit drug use more for females than for males. Broidy and Agnew (1997) argued that females would turn to illicit drug use more so than males in the face of strain because the strain would cause feelings such as guilt and depression. Some females may feel the need to remedy these feelings and may be more likely to engage in self-destructive forms of behavior such as illicit drug use. It would follow that victimization would cause a greater effect on drug use in females than in males. The results of this study do not support this. It was found the victimization seemed to be more important in predicting illicit drug use in males than females. However, this finding must be viewed with caution. Again, this could be due to the aforementioned gender differences in the scale measuring victimization. The difference found in this study could possibly be due to gender differences in the reliability of the victimization scale rather than an actual gender difference. Also, the way the illicit drug use variable was calculated may provide a reason for this. Illicit drug use was made up of the use of cocaine, marijuana, heroin, barbiturates, amphetamines, and hallucinogens. Perhaps the females in this sample turned to other drugs such as prescription drugs as a response to strain. This study would have no way of determining if victimized females turned to any other type of substance as a response to being victimized. Also, there are some studies, like the present study, that find the opposite of what Broidy and Agnew (1997) hypothesized. For example, Harrison et al. (1989) found that victimized males used inhalants more than non-victimized males. However, there was no difference for females. Therefore, the finding in the present study that victimization is more predictive of illicit drug use in males than females does have its precedent in the literature. Clearly, future research is needed to examine the gender differences in this area. This study also found that victimization increased nonviolent/nondrug offending more so for females than for males. This finding is the opposite of Broidy and Agnew’s (1997) arguments regarding gender, train and crime. They argued that males are more likely than females to experience financial strain that could lead to more property crime in males; a type of offending that was included in the definition of nonviolent, non-drug offending in the present study. Also, this finding is unusual consider that nonviolent/non-drug offending and victimization was found to be more reliable for males than females.

146


This study’s finding is also different from the findings of the few studies that have looked at gender differences in the relationship between victimization and nonviolent, non-drug offending. For example, Widom and White (1997) found that there was no gender difference in the effect of victimization on nonviolent arrests. However, their study only considered childhood victimization. It could be possible that a gender difference may appear as a result of victimization during the adolescent years, but not be sensitive to childhood victimization.

LIMITATIONS OF STUDY There were several limitations in the present study that must be taken into account when considering these results. One serious limitation of this study is the gender and racial differences in reliability of several of the variables in the study, particularly the scale measuring victimization and also differences in the samples sizes of the racial groups in the panel. These differences could have serious affects on whether racial or gender differences in the victimization/delinquency relationship actually exist. Therefore, the findings are tentative at best and should be interpreted with caution. Also, there were no indicators of model fit or explained variance calculated. Furthermore, there were no power analyses done to determine the statistical power of any of the models. Nor were there any statistical tests between coefficients and models to determine if there were statistically significant differences between coefficients and models. All of these would have been able to shed some light on the results and could help to determine with more precision if and gender or racial differences actually exist. But again, with out these, the results of the study should be viewed with caution. Another limitation is that this study only considered the effect of victimization on various types of delinquency for Whites and African Americans. It did not consider other ethnic groups such as Hispanics and Asians. This was due to the low numbers of individuals who reported being part of racial groups other than White and African American. There is evidence that the victimization/delinquency relationship may be different for these groups. Perez (2001) found that the effect of sexual assault was more pronounced for Whites than Hispanics.


147

Another weakness is that this study only had one definition of victimization. The reason for this was that there was only one item on the NYS questionnaire administered during the first four waves that dealt with sexual victimization and several that dealt with physical victimization. It seemed feasible to combine them all into one variable measuring victimization. Therefore, it is impossible to determine which type of victimization was most important in predicting a particular type of delinquent behavior. Also, one of the dependent variables was a composite of measures of use of various illicit drugs. Therefore, this study is unable to determine whether victimization affected a particular type of illicit drug, such as cocaine, greater than another. This study also did not look at the effect of victimization on use of alcohol. There were two reasons for this. The primary reason for the lack of investigation into alcohol use is how alcohol use is measured in the National Youth Survey. In some waves of the NYS, frequency of alcohol use was measured by using a variety of questions regarding use of a variety of alcoholic beverages. In other waves, the frequency of alcohol use was measured by a single question regarding frequency of use (Elliott et al. 1989). Due to the lack of consistency with the measurement of alcohol use in the NYS, it was decided not to pursue an examination into the effect of victimization on alcohol use. And finally, this study was unable to determine if the victimization reported by the individuals in the study was an extension of victimization that started during childhood or if the victimization occurred only during the years that the adolescents participated in the NYS. In other words, this study did not have access to information regarding victimization that occurred prior to the beginning of the NYS. Therefore, it is unknown if the length of victimization had an effect on delinquency in the panel used for this study. With all of these limitations to this study, it is strongly encouraged to use caution and to remember these drawbacks of this study when reading and interpreting the results of this study.

DIRECTIONS FOR FUTURE RESEARCH Several directions for future research can be noted. One is that more studies should look into the differences in the effect of victimization taken place only during childhood, during the childhood and adolescent

148


years, and victimization that occurred only during adolescence. To date, there have been only two studies that have looked at this (Ireland et al. 2002; Thornberry et al. 2001).2 They both discovered that the effects varied based on when the victimization took place. They found that the effect of victimization on delinquency was greatest when the victimization occurred during adolescence. Clearly, more work is needed to confirm these findings. In addition to Whites and African Americans, more research is also needed to examine the effect of victimization for a variety of ethnic groups. Most work does not even examine race and ethnicity differences, and for those that do, most studies simply compare African Americans with Whites (Kruttschnitt and Dornfeld 1991; Maxfield and Widom 1996; Rivera and Widom 1990; Widom 1989a). Perez’s (2001) work is just one of a few that included the effect of victimization on delinquency in a racial group that was neither African American nor White. More work needs to be done to consider the victimization/delinquency relationship on non-White, non-African American groups to determine if a different pattern exists for individuals who are neither White nor African American. Also, more studies are needed to look at the effect of victimization on nonviolent, non-drug offending. Of the studies on property offending mentioned earlier in this volume, only two were based on longitudinal data (Menard 2002; Stouthamer-Loeber et al. 2001). Both works suggest that victimization does increase the amount of some types of nonviolent, non-drug offending over time. More research is needed to confirm these findings. Another needed avenue of research is to examine the effect of victimization on the use of specific types of drugs. Most studies, including the present study, do not attempt to discover whether victimization affects the use of one type of drug more so than another type (Dembo et al. 1987; Kilpatrick et al. 2000). Research needs to be done to see what types of drug use that victimization leads to. Yet another avenue of future research relating to illicit drug use alluded to earlier is that more work needs to be done to more clearly define how the relationship between victimization and illicit drug use in adolescents varies according to gender. Theory suggests that females are more likely to turn to drug use than males when subjected to victimization (Broidy and Agnew 1997; Horowitz and White 1987). However, the research literature is unclear as to whether these


149

arguments are correct. More studies are needed to show if a gender difference exists. If one does exist, work needs to be conducted to determine the exact nature of this difference.

POLICY AND THEORETICAL IMPLICATIONS There are several policy and theoretical implications that can be taken from this study. One policy implication of this study is that child protection agencies need to provide some sort of intervention for victimized adolescents in order to decrease the likelihood that they would go on to participate in delinquent behavior. In helping adolescents who have been victimized, child protection agencies could implement programs that would encourage more values that are disapproving of deviant behavior. Other programs used by such agencies for victimized adolescents could also include family counseling that would teach family members of a victimized adolescent to provide more support for that individual. This study also shows that juvenile courts may need to use a more holistic approach in dealing with delinquents. Consistent with other research (Fagan et al. 1987; Mouzakitis 1981; Vissing et al. 1991), this study shows a link between being victimized and engaging in delinquent behavior as a juvenile. In sentencing delinquents, juvenile courts may look to see if the delinquent had been victimized. If it is found that a delinquent had been victimized, instead of or in addition to institutionalizing that individual, providing counseling to address the victimization which may be beneficial in decreasing the likelihood of recidivism. And finally, an important theoretical implication of the present findings is that Agnew’s general strain theory may apply differently to different racial and gender groups. As mentioned earlier, more testing is needed to confirm the racial differences found in this study.


Endnotes

1.

This study is now referred to as the National Youth Survey Family Study. However, through this text, it will be referred to as the National Youth Survey or NYS.

2.

Databases searched: Criminal Justice Abstracts, Expanded Academic ASAP, Family and Society Studies Worldwide, ISI Citation Database, National Criminal Justice Reference Service Abstracts, PCI (Periodicals Contents Index), PsycINFO, Social Sciences Citation Index, Sociological Abstracts, JSTOR.

3.

Scott Menard is the current principal investigator for the study (Institute for Behavioral Science 2006).

4.

The institute is currently collaborating with the Institute for Behavioral Genetics and the Center for the Study of Prevention of Violence both at the University of Colorado at Boulder to collect data (Institute for Behavioral Science 2006).

5.

The NYS has also received funding from the National Institute on Alcohol Abuse with supplemental funding from the National Institute on Drug Abuse, the National Institute of Justice, and the National Institute of Mental Health.

6.

An age, sex, and race comparisons between nonparticipating eligible youth and those who decided to participate in the NYS done by the researchers showed that the loss rate from any particular age, sex, or racial group appears to be proportional to that group’s representation in the population. Based on these characteristics, the participants appeared to be representative of the total 11 through 17 year-old youth population in the U.S. as established by the U.S. Census Bureau at the time (Elliott et al. 1988).

151

152 7.

Endnotes Growth curve modeling can also be seen as a version of structural equation modeling (Kaplan 2000). However, because moderation of the predictors was used, this study used another approach to the technique.

Bibliography

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APPENDIX

Missing Data Analyses

Missing data posed a potential problem in the present study. Even though Raudenbush and Bryk (2002) argue that growth curve modeling can be done with incomplete longitudinal data, one must be aware of the amount of data that is missing and any factors that may distinguish between staying in a longitudinal study and dropping out of the study. If any factors are found to be related to missing data, those factors must be considered when viewing the results of this study. To determine what factors played into the cause of missing data, the sample of 1,203 individuals in this study were split into two groups: those with complete data (data on all variables used in the present study at each of the first four waves of the National Youth Survey), and those with incomplete data (missing data on at least one of the variables used in the present study from waves two through four of the National Youth Survey). Next, the means of various wave one variables were compared to determine if any differences between the groups. There were 137 adolescents of the 1,203 in this study with incomplete data. Among them, 62% were male and 80% were White. Also, means of both groups were tabulated on the association of delinquent peers, SES, social support, goals, conforming values, nonviolent, non-drug offending, violent offending, victimization, drug use and age. The means revealed that those complete data and those with incomplete data were nearly the same age (13.28 for those with incomplete data versus 12.99 for those with complete data). Also, there was almost no difference between the groups in terms of drug use (mean of .63 for those with incomplete data versus a mean of .61 for those with complete data). Furthermore, in terms of association with delinquent peers, there was almost no difference between the two groups (8.67 for those with incomplete data versus 8.58 for those with complete data). Also, in terms of social support there was almost no 163

164

Appendix

difference between the two groups (16.36 versus 16.41). In addition, in terms of importance of societal goals there was very little difference between the two groups (7.14 versus 7.44). In terms of conforming values, there was very little difference between the two groups (22.01 versus 21.78). There were some apparent differences between the two groups in terms of certain types of offending and socioeconomic status. Those with incomplete data reported, on average, more violent offending (1.90 versus 1.02). They also reported more nonviolent, non-drug offending (3.26 versus 2.21). Also, those with incomplete data were more likely to come from homes with less reported family income than those with complete data.

Index

abuse. See victimization Agnew, Robert, 9–15, 18, 25, 44, 53, 56, 68, 87, 90, 106, 140, 142 Akers, Ronald, 13–14 anomie, 6–9 Arellano, Charleana, 30–31 blockage of legitimate means, 7 of pain avoidance, 9 Bryk, Anthony, 72–80 Cloward, Richard, 7–8 Cohen, Albert, 8–9 cycle of violence, 5, 27 data, 41–44 count, 73–75, 78 longitudinal, 72, 75, 78, 148 missing, 46–67, 64, 67, 69, 71 nested, 72, 76, 77 delinquency drug use, 29 general, 23–27 non-violent, non-drug, 33–36 violent, 27 delinquent behavior. See delinquency Dembo, Richard, 29–32 deviance instrumental, 9

non-instrumental, 9 Doerner, William, 23, 26 Durkheim, Emile, 6 Elliott, Delbert, 41–42 Finkelhor, David, 26, 34 General Strain Theory (GST), 5–13 adaptations to strain, 10 constraining/conditioning factors, 15, 56, 62, 68, 69, 82, 140 gender differences, 18, 29, 36–39 racial differences, 18, 29, 36– 39 growth curve modeling, 72–81, 85, 89 hierarchical generalized linear modeling (HGLM), 72–76, 78 Hirschi, Travis, 13–15 hypotheses, 39, 140–46 implications, policy and theoretical, 149 intercepts-and-slopes-asoutcomes model, 81–84 maltreatment. See victimization McCord, Joan, 24, 34 means, illegitimate, 7 165

166 means, legitimate, 6 Merton, Robert, 6, 10 missing data. See Appendix; also see data, missing National Youth Survey (NYS), 16, 41–44, 45, 48, 51, 53, 56, 58, 60, 61, 62, 65, 66, 67, 71, 74, 85, 103, 120, 147 offending. See delinquency outliers, 45–46, 47, 49, 50, 51, 54, 70 overdispersion, 78, 79 Poisson distribution, 74, 77, 79 random coefficients model, 85 Raudenbush, Stephen, 72–80 reliability, 69–71, 101, 102, 119, 120, 138, 143–46 research questions, 3, 140–46 social control theory, 13–15 social learning theory, 13–15 societal goals, 6–10, 18, 58–60, 138, 142 statistical modeling. See hierarchical generalized linear modeling (HGLM), growth curve modeling, interceptsand-slopes-as-outcomes model, and random coefficients model strain adaptations to. See General Strain Theory sources of, 10, 36, 87, 140

Index Thornberry, Terence, 1, 22, 24, 26, 27, 32, 148 variables, control age, 67 gender, 65, 144 race, 65, 143 socioeconomic status, 66 variables, dependent, 44 illicit drug use, 51, 80, 84, 142, Chapter 6 nonviolent, non-drug offending, 48, 141, Chapter 7 violent offending, 44, 141, Chapter 5 variables, independent conforming values, 56 delinquent peers, 62 social support, 61 societal goals, 58 victimization, 53 variables, interaction, 68 victimization adolescent, 25–27 childhood, 23–24 sexual, 34 victimization/delinquency relationship GST, 17 race/gender differences, 36– 39 Widom, Cathy Spatz, 5, 23, 27, 31, 33, 34, 37

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