Criminal Justice Recent Scholarship
Edited by Marilyn McShane and Frank P. Williams III
A Series from LFB Scholarly
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Adolescent Problem Behaviors Delinquency, Aggression, and Drug Use
Ricardo M. Marte
LFB Scholarly Publishing LLC New York 2008
Copyright © 2008 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Marte, Ricardo M., 1973Adolescent problem behaviors : delinquency, aggression, and drug use / Ricardo M. Marte. p. cm. -- (Criminal justice, recent scholarship) Includes bibliographical references and index. ISBN 978-1-59332-269-4 (alk. paper) 1. Juvenile delinquency--United States. 2. Aggressiveness in adolescence--United States. 3. Drug abuse--United States. I. Title. HV9104.M2653 2008 364.360973--dc22 2008011511
ISBN 978-1-59332-269-4 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.
Table of Contents 1. Adolescent Problem Behaviors .......................................................... 1 2. Theoretical Framework and Model Development ........................... 13 3. Testing the model: A multi-state U.S. sample ................................. 91 4. Critical Findings ............................................................................ 111 5. Discussion and Implications .......................................................... 161 Appendix A: Survey Origins & Objectives ....................................... 189 Appendix B: Arrestable Offenses ...................................................... 191 Appendix C: Preliminary Analyses.................................................... 199 References.......................................................................................... 235 Index .................................................................................................. 251
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CHAPTER 1
Adolescent Problem Behaviors
Despite a decrease in overall adolescent crime during the last decade offense rates remain above those of the 1980s (Snyder & Sickmund, 1999). Recent self-report (CDC, 2002; Youth Online: Comprehensive Results, 2007) and arrest data (FBI, 2002) also indicate that crime patterns among adolescents have not been entirely positive; less lethal forms of juvenile offenses (e.g., weapons-carrying) and drug use have remained resistant to decline or have recently increased. These data suggest that while most adolescents may be safer from lethal forms of violence, they are spending increasing amounts of time unsupervised, at heightened risk for non-lethal-victimization (fighting, sexual assault) and participation in problem behaviors (e.g., drug use and weapons use). The unacceptably high rates and changes in the type of offending also suggest that further study of the factors and processes that explain adolescent problem behaviors is necessary. The current project addresses the need for further study of the etiology of problem behaviors by developing and testing an ecological model of problem behaviors. Before a model can be developed, several issues need to be addressed, including: The definition and relationship between delinquency and problem behaviors, the need for study on general population samples, and the need for an adapted ecological (i.e., social systems) framework that distinguishes between psychological and behavioral outcomes. The first two issues are presented here and discussed throughout the study. The adapted social systems framework is noted here and developed fully in Chapter 2.
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DELINQUENCY AND PROBLEM BEHAVIORS Adolescent crime has been studied using many labels. The most common label for adolescent criminal behavior has been delinquency (Gottfredson, Sealock, & Koper, 1996). Delinquency encompasses a range of norm-breaking behaviors that apply to adults and minors. Behaviors for which adults are criminally responsible include drug use and violent offenses against other persons (e.g., assault), property (e.g., vandalism, arson), and public order (Bartol & Bartol, 1989). In addition to criminal violations, minors also are responsible for status offenses1 (e.g., truancy, community curfews, running away; Bartol & Bartol, 1989; Bartollas, 2003). Tables B1 and B2 in Appendix C list 28 offenses for which an adolescent may be arrested or prosecuted (FBI, 2002; Bartollas, 2003). In sum, delinquency refers to a range of behaviors that, when committed by a person who has not reached adulthood, violate a law and elicit a legal response from the community. Delinquency also has been operationalized as deviance, anti-social behavior or as a component of problem behaviors (Farrell, Kung, White, & Valois, 2000), interpersonal aggression (Griffin et al., 1999), drug use (Lonczak et al., 2001), and a combination of these behaviors (Gorman-Smith, Tolan, Loeber, & Henry, 1997; O'Donnell, Hawkins, & Abbott, 1995; Jessor & Jessor, 1977). Others (Elliott et al., 1987) have operationalized delinquency based on the seriousness of the offense, as well as on the frequency and rate of offending. Overall, the range of delinquent (i.e., arrestable) offenses2 is varied and encompasses serious interpersonal violence and minor property damage. These adolescent behaviors are not always studied in relation to a violation of a given law. Instead, engaging in these behaviors is sometimes conceptualized as reflecting an underlying pattern of activities that run counter to acceptable social norms and are specific to 1
Department of Justice: “An act committed by a juvenile for which an adult could be prosecuted in a criminal court, but when committed by a juvenile is within the jurisdiction of the juvenile court. Delinquent acts include crimes against persons, crimes against property, drug offenses, and crimes against public order, as defined under Referral offense, when such acts are committed by juveniles.” 2 A list of 28 arrestable offenses is provided in Appendix B.
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the period of adolescence (Jessor & Jessor; 1977; Jessor et al., 1995; O'Donnell, Hawkins, & Abbott, 1995). Jessor and Jessor's (1977) Problem Behavior Theory (PBT), for example, is an explanatory theory of both conventional (i.e., prosocial) behaviors and socially 3 undesirable, problem behaviors. Problem behavior, according to the Jessors, is comprised of a range of conduct including interpersonal aggression, delinquency (e.g., property crimes), drug use, and sexual promiscuity. Furthermore, the Jessors define problem behavior not as a value judgment, but as a set of adolescent behaviors that: a) in general, run counter to social norms, b) are viewed by the larger society as a cause for concern, and c) elicit efforts by the larger society to control. Thus, the Jessor’s definition of problem behavior is synonymous4 with more applied descriptions of the same behaviors, such as delinquency. The current study builds on the synonymity between delinquency and problem behaviors in several ways. First, the study reviews existing theories of delinquency and integrates them using a social systems framework used to explain problem behaviors5. Second, the outcome measures in this study (interpersonal aggression, propertyrelated delinquency, and drug use) constitute arrestable offenses6 consistent with the definition of delinquency as “the violation of legal code, including those relative to age (Catalano, Kosterman, Hawkins, & Newcomb, 1996, p. 430).” Third, the behavioral outcome-measures encompass three factors (drugs, delinquency, and aggression) which have been identified as corresponding to a higher-order construct referred to as problem behavior (Farrell, Kung, White, & Valois, 2000; Jessor & Jessor; 1977; Jessor et al., 1995; O'Donnell, Hawkins, & Abbott, 1995). Finally, these outcome measures also are behaviors that run counter to social norms, are a source of societal concern, and elicit societal efforts to control them (OJJDP, 2005)—all characteristics that are consistent with the operationalization of delinquency in previous studies (Jessor & Jessor; 1977; Jessor et al., 1995; O'Donnell, Hawkins, 3
Socially refers to broader society and not necessarily the interpersonal norms that may exist in adolescent cliques or groups. 4 The Jessors’ definition also is consistent with the set of delinquent behaviors listed on Table B1 in Appendix B. 5 The framework can also be used to explain positive outcomes as well (Jessor and Jessor, 1977). 6 In other words, criminal and status offenses in each of the four jurisdictions (Arizona, California, Nevada, and Wyoming) where the data were collected.
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& Abbott, 1995). The interchangeable use of delinquency and problem behaviors in this study is not intended to promote confusion. Instead, the purpose is to reflect the varied nature of the operational definitions of adolescent behaviors as they occur in the literature. Problem Behaviors and General Population Studies Research has shown that children who engage in problem behaviors are likely to be exposed to multiple risk factors (Loeber & StouthamerLoeber, 1998). Multiple-risk factors (e.g., childhood defiant behavior, impulsivity, parental impulsivity, and aggressiveness) contribute directly to engagement in problem behaviors (Sampson & Laub, 1990) as well as other negative outcomes (e.g., undermined parenting practices). These other negative outcomes, in turn, further contribute to problem behaviors as when lax parental monitoring leads to negative peer associations (Simons, Chao, Conger, & Elder, 2001) and overall declines in monitoring of negative behaviors (Lytton, 1990; Sampson & Laub, 1990). The earlier a child engages in problem behaviors, the more likely he or she is to continue such behaviors throughout his or her adolescent and adult years (Espiritu, Huizinga, Crawford, & Loeber, 2001; Farrington, Lambert, & West, 1998; Kempf-Leonard, Chesney-Lind, & Hawkins, 2001; Krohn, Thornberry, Rivera, & Le Blanc., 2001; Loeber, 1983, 1988; Loeber & Farrington, 1998b; Moffitt, 1993). In addition to the negative impact criminal behaviors have on offenders, their families, and their victims, there are external costs to society in terms of loss of productivity, legal costs, treatment, rehabilitation, and/or incarceration. A study by Cohen (1998) estimated the external cost to society, in 1997 dollars, to be between $1.7 and $2.3 million per high school dropout who becomes a career criminal. The aforementioned trends in adolescent criminal behavior, as well as the social and economic impact those behaviors have on society, underscore the need for further research into the causes and processes surrounding adolescent problem behaviors. Despite concerns for adolescents who commit serious offenses and go on to become career criminals, less serious offenses are the most common among general population samples and include running away, curfew violations, and alcohol and tobacco use (CDC, 2002). Elliott et al. (1987). In addition, researchers (Cernkovich, Giordano, & Pugh, 1985; Wolfe & Shoemaker, 1999) have found that a small group of
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repeat offenders are responsible for most crimes—and this small group of repeat offenders has been found to have the fewest protective7 factors from which to draw (Pollard, Hawkins, & Arthur, 1999). General population studies have a larger balance of adolescents who have committed fewer crimes and who presumably have more protective influences in their lives that discourage delinquency, a form of problem behaviors in this study. Thus, studies focusing on general populations that have been identified as having risk factors for delinquency (e.g., gang proliferation, economic decline) are needed to help elucidate the various protective mechanisms that may help inhibit some adolescents from engaging in problem behaviors (Bronfenbrenner, 1979). To address this need, the data for this study derive from a general multi-state sample of middle-school adolescents. Care was taken to target school sites with higher potential rates of problem behaviors and to stratify sites by rural/urban status. Adapted Social Systems Framework Though explanatory theories of problem behaviors abound from a variety of single theoretical perspectives (Becker, 1966; Cloward & Ohlin, 1955; Hirschi, 2002; Matza, 1964; Merton, 1957; Reckless, 1961; Shaw & Mckay, 1949; Sutherland, 1939), social scientists have sought to integrate these individual theories in order to explore the causes of problem behaviors from a multi-disciplinary perspective (Elliott, Ageton, & Canter, 1979; Gottfredson & Hirschi, 1990; Hawkins & Weis, 1985; Jessor & Jessor, 1977; Shoemaker, 2000; Thornberry, 1987). Two advances in social science have influenced the direction of research on problem behaviors. First, a paradigm shift (Richardson, 2002) towards resiliency-focused research that began in the 1970s has consistently called for the study of protective factors (Zimmerman & Arunkumar, 1994) that inhibit problem behaviors and promote positive outcomes.
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Discussed under resiliency research, below. A protective factor refers to a variable that offsets the negative impact of a risk factor, resulting in a positive outcome. For example, peer pressure is a risk factor for delinquency but its influence can be offset by parental monitoring. Adolescents may be less likely to give into negative peer pressure if they believe their parents are aware of their actions.
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Second, the social systems theory or ecological approach (Bartol & Bartol, 1989; Bronfenbrenner, 1979; Jessor & Jessor, 1977, Muuss, 2000), provided a framework within which more comprehensive studies of adolescent behavior, including prosocial and problem behaviors (e.g., delinquency, aggression, drug use), could be developed. A social systems theory (i.e., ecological) approach refers to the study of the manner in which systems, within a person’s social and physical environment (i.e., ecology), interact to determine that person’s development and behavior. Social systems theory includes macro, exo, meso, and micro-systems that encompass contextual to interpersonal influences on individual development (Bronfenbrenner, 1979). Specifically, the systems theory framework has served as a means of understanding how hypothesized risk and protective factors to adolescent (positive or negative) behavior interact across different ecological levels (personal, interpersonal, contextual) to explain human development. According to Bronfenbrenner (1979), malleable forces that are external to an individual, can be more effective avenues of positive developmental change, compared to fixed internal characteristics. For example, membership in a given racial group may be associated with negative developmental outcomes. A change towards a positive developmental outcome clearly cannot be accomplished by changing racial membership; but could be accomplished via changes to external factors that affect that particular racial group (e.g., discriminatory access to resources, cultural responses to inequity). By extension, problem behavior prevention efforts that focus on changing behaviors are successful when those efforts focus on malleable influences. Other researchers (Bartol & Bartol, 1989) have emphasized the importance of internal characteristics (i.e., biological and personality) on adolescent behavior. Bartol and Bartol proposed the addition of an infra-system to Bronfenbrenner’s social systems framework to better reflect the role of individual differences in development. Despite these proposed changes, there remains a lack of conceptual clarity regarding the role of individual differences and behaviors in a systems theory framework of human development. Specifically, in terms of model development, it is unclear whether individual differences or behaviors are outcome variables, or whether each one is an explanatory factor for the other. The selection of protective factors studied in a system theory framework also has been limited. Whereas an increasing number of
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problem behavior studies integrate protective factors, few studies attempt to capture the impact of these factors at multiple conceptual (i.e., system) levels (Griffin et al., 1999; Lonczak et al., 2001). The current study addresses these limitations in the literature by: a) adapting a social systems theory framework to more adequately describe the roles of personal level (i.e., individual differences) factors and behavior, b) developing an explanatory model of problem behaviors (i.e., delinquency, aggression, and drug use) based on this adapted framework, c) including multiple protective factors that operate at the personal and interpersonal levels, along with d) risk factors that operate at the interpersonal and contextual levels. Figure 1.1 illustrates the process involved in developing the explanatory model of problem behaviors guiding8 this study. The initial step in model building involved adapting Bronfenbrenner's (1979) social systems framework from an explanatory model of human development to an explanatory framework of human behavior. Chapter 2 describes the process through which Bronfenbrenner's four systems (i.e., micro-, meso-, exo-, and macro-), and Jessor and Jessor's (1977) three systems (i.e., personality, perceived environment, and behavior) were combined with Bartol and Bartol's (1989) infra-system. The combination of these social system components forms the revised systems theory framework comprised of six sub-systems: macro-, exo-, meso-, micro-, infra-, and behavior-systems. The addition of the behavior-system a) extends the work of Bartol and Bartol, b) marries social systems theory and problem behavior theory, c) and transforms the framework from a developmental model to one that can be used to explain behavior. Figure 1.2 illustrates the conceptual model that guided this study. The rings in Figure 1.2 represent the different systems of the revised theoretical framework. The rings also denote the different conceptual (i.e., levels of) forces that influence human behavior. For example, the macro-system refers to macro-level factors that may affect adolescent behavior, whereas the infra-system refers to personal level factors (e.g., locus of control) that also may influence behavior. The outcome measure (e.g., problem behavior) is located within the behavior system. The personal, interpersonal, and contextual variables located across the different systems can take the form of risk or protective factors. The 8
Although a brief description is given here, a detailed discussion of the model’s development is provided in Chapter 2.
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hypothesized relationships between these risk and protective factors combine to produce negative or positive (i.e., resilient) outcomes in the behavior system. The selection, location, and relationships between factors were determined from the literature on delinquency and problem behaviors as well as the underlying premises of the social systems framework. Following the discussion of the development of an adapted systems theory framework, the focus turns to developing a model of problem behaviors that is based on this framework. To begin, personal, interpersonal, and contextual theories of delinquency and problem behaviors are discussed with respect to their location within the adapted social systems framework. An integrative model of delinquency, proposed by Shoemaker (2000), also is used to review the literature on individual factors of delinquency. The factors in Shoemaker’s model are discussed in terms of their location in the adapted systems theory framework9. The purpose of this exercise was to delineate keyed constructs (i.e., explanatory factors) purported to be associated with problem behaviors and to determine their causal location in relation to problem behaviors within the new framework. This step is crucial for sound model development (Hirschi & Selvin, 1978; Shoemaker, 2000). Shoemaker’s integrative model was selected for this purpose for three reasons. First, the model is not based on any perspective; but merely reflects the empirical evaluation (Shoemaker, 2000) of prominent individual theories. Second, the model remains untested. While this study is not a test of Shoemaker’s entire model, an evaluation of some portion of that model would be a contribution to the literature. Third, the range of delinquent behaviors that the model purports to explain is consistent with activities that comprise problem behaviors in this study: (non-violent) delinquency, aggression, and drug use. The result of the theoretical integration of the adapted systems framework, individual theories of delinquency and problem behaviors, and Shoemaker’s Integrative Model of delinquency, is a general conceptual model of problem behaviors.
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You may refer to Figures 2.6 and 2.7 in Chapter 2.
Figure 1.1 Processes and Components for the Development of an Ecological Model of Adolescent Problem Behaviors (Delinquency, Aggression, & Drug Use)
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Figure 1.2 Conceptual Model of Adolescent Problem Behaviors (Delinquency, Aggression, & Drug Use)
Hypothesized Ecological Model of Problem Behaviors After developing a general conceptual model of problem behaviors, the next step was to identify the actual risk factors, protective factors, and outcome measures that would be used test the hypothesized model in this study. The measures derive from an existing (i.e., secondary) data set of 1286 eighth grade students’ responses to questions about delinquency, aggression, drug use, and other factors related to neighborhood, family, peer, and personal influences10. Measures (e.g., scales) that corresponded with the factors in the general conceptual
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To ensure interpretable results, pathways that were not logically consistent with the operationalized definitions of the measures in this study were removed and noted.
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model were selected and tested for internal consistency11. Figure 1.3 illustrates the final set of factors included in the hypothesized model of problem behavior, superimposed on a systems theory framework.
Figure 1.3 Hypothesized Ecological Model of Problem Behaviors (Delinquency, Aggression, & Drug Use)
The overall set of relationships depicted by the hypothesized model represents the main research question for this project: Is the etiology of problem behaviors adequately described by an ecological model comprised of personal, interpersonal, and contextual risk and protective factors? Subsumed within this research question are seven hypothesized mediated pathways linking a contextual risk factor (in the exo-system) to problem behaviors (in the behavioral system). These 11
Refer to Appendix C for a complete set of analyses.
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pathways are developed in Chapter 2 and their results discussed in relation to the overall framework and the current literature on problem behaviors. The model also is tested and compared among male, female, rural, and urban samples, to determine any potential moderating effects of gender and location. The scope of the model is discussed in Chapter 2 and limitations of the current study are discussed in Chapter 5. The implications of the current study for future research and prevention efforts regarding problem behaviors are discussed in relation to gender, location, and race. Special attention is given to the potential contributions of the adapted social systems framework to the problem behavior literature. Suggestions regarding the use and benefits of alternate data collection methods in conjunction with self-report measures that rely on the perceived environment also are discussed. Finally, longitudinal applications of the adapted framework and assessments of moderating effects within that framework are discussed as potential contributions to the literature on resiliency.
CHAPTER 2
Theoretical Framework and Model Development
BRIEF HISTORY OF DELINQUENCY IN THE U.S. The history12 of delinquency in the United States can be traced back hundreds of years (Bartollas, 2003; Platt, 1969, Sanders, 1970). Bartollas (2003) identifies seven periods in American history that reflect changes in the way delinquency was understood and offenders were thus treated. Simpler communal laws characterize the Colonial Period between 1636 and 1823, when infractions were handled within the family. Communal violations that continued after family intervention resulted in public punishment (e.g., corporal punishment, community expulsion, or capital punishment). Penal institutions and law enforcement institutions were limited to local town “magistrates, sheriffs, and watchmen” (Bartollas, 2003, p. 16). Incarcerations were temporary interventions prior to trial or sentencing. The years between 1824 and 1898 represent ‘The Houses of Refuge Period’ when the initial functions of reformation and punishment of young offenders was no longer left to the family, but relegated to external institutions (Bartollas, 2003). Social reformers intended to use these houses to protect at-risk children from criminal adulthoods by: a) removing children from families with undisciplined or criminal parents and b) providing swift and harsh punishment for juvenile infractions. Modeled by Quaker reformers, many of these 12
The subsequent historical review is based mostly on Bartollas (2003).
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houses of refuge were intended to provide education to wayward children until the age of 21. However, children in these houses often received four hours of schooling per day, two of which were devoted to religious purposes. An additional eight hours per day were devoted to harsh labor the house administrators subcontracted with local factories (Bartol & Bartol, 1989). Despite their early popularity, by the mid 19th century, houses of refuge began to lose support among social reformers when it became apparent that these houses had come to resemble jails, provided extremely harsh living conditions, and had shown little effect in curbing delinquency (Bartol & Bartol, 1989). House of refuge administrators attributed their failure to the influence of older and unreformed offenders on younger delinquents. Administrators suggested that unsalvageable, older adolescents should be separated from younger juveniles (i.e., under 16 years of age) who had an opportunity to reform. Ultimately, the failure of the Houses of Refuge Movement led to greater government involvement in the handling of juvenile delinquents. The term ‘juvenile delinquent’ became officially embedded in American society by 1899, when the first juvenile court was established in Chicago, thus initiating the third period in American delinquency. The years between 1899 and 1966 mark the ‘Juvenile Court Period’ wherein the courts operated under the English doctrine of parens patriae. This new doctrine required that American courts intervene in a child’s best interest and provide family services when such services were not being provided and the lack of such services were deemed to contribute to future delinquency (Bartol & Bartol, 1989). Specifically, the Juvenile Court act of 1899 served to: a) refine the definition of delinquency, b) remove juvenile cases from criminal court, c) separate temporarily institutionalized juveniles from adults offenders, and d) establish a probation system for juveniles to minimize institutionalization (Bartol & Bartol, 1989; Bartollas, 2003). The development of juvenile court continued to emphasize many of the conceptual changes brought about by the Houses of Refuge Movement, including the ideas that the state superseded the role of the family when handling juvenile punishment and reform, and that punishment and reform could continue to involve institutionalization. According to this new perspective, however, the role of court intervention was to decriminalize youthful offenders and help save them from factors that contributed to crime including poverty, urbanicity, and dysfunctional
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family life (Bartol & Bartol, 1989; Bartollas, 2003). By the mid 1920s, all but two states (Maine and Wyoming) had developed juvenile courts modeled after the Chicago Juvenile Court. A fourth period in American delinquency, the ‘Juvenile Rights Period,’ extended briefly between 1967 and 1975. It was during this time that landmark cases afforded adolescents constitutional rights previously limited by arbitrary juvenile court procedures and decisions (Bartollas, 2003). In one such case (In Re Gault, 1967), the Supreme Court held that 15-year-old Gerald Gault was not afforded due process when he was arrested for making a lewd phone call to a neighbor in 1964 and sentenced to 6 years in a juvenile institution13. The Supreme Court decision emphasized that an adolescent could not be stripped of due process when a conviction could result in criminal penalties (e.g., institutionalization). On the heels of changes to juvenile due process and the Delinquency Prevention Act of 1974, social reformers made additional strides to reduce abusive police contact with adolescents, lengthen juvenile court hearings to provide adequate consideration, improve treatment at training schools, and provide alternatives to trainings schools (Bartollas, 2003). The Delinquency Act of 1974 established grant funding for delinquency prevention efforts, the separation requirement prohibiting incarceration of juveniles among adults offenders, and the deinstitutionalization of status offenders (DSO) (OJJDP, 2003). Whereas violent or serious offenders continued to be institutionalized, less serious offenders were sentenced to probation and community-based programs. In the mean time, state involvement in welfare of minors continued to increase. By the late 1970s, the focus of children’s advocacy groups extended to areas of child abuse, custody, medical care, and privacy issues. The 5th period that Bartollas (2003) describes as the ‘Reform Agenda of the 1970s’ was not entirely positive. In its first five years, only ten percent of the budgeted grants established by the Delinquency Act of 1974 went to programs addressing serious offenders. That funding policy had sentencing implications when serious juvenile 13
Specifically, Gault’s parents were never contacted, he was not informed of the charges, he was never provided lawyers, he was never allowed to confront or cross examine his accuser, and he was forced to self-incriminate.
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crimes received great media attention during a subsequent conservative presidential administration. As serious adolescent crime, drug use, and pregnancies increased, a policy of stricter parenting and harsher punishment had become more popular (Bartol & Bartol, 1989; Bartollas, 2003). Previous efforts and programs to decriminalize status offenders and remove juveniles from jail were at risk of disappearing as new administration policies sought to limit government funding (except for delinquency research). By the 1980s, Presidential policy efforts also sought new approaches to delinquency prevention that were in stark contrast to the 1970s. These policy efforts mark the 6th ‘Social Control’ period of American delinquency and included: (a) preventive detention for imminent delinquent behavior, (b) relocation of offenders from juvenile to adult criminal court, (c) definitive, mandatory sentencing of violent juvenile offenders, (d) increased confinement of juvenile offenders, and (e) death penalty sentencing and enforcement for juveniles convicted of murder. During the same period however, juvenile courts adopted a model consistent with previous reforms from the 1970s (Bartollas, 2003). In this 6th period, the courts also viewed delinquency as a product of three causes: uncontrollable behavior, thrill-seeking, and rational choice. Adolescents who committed delinquency due to uncontrollable behaviors were considered status or minor offenders and provided treatment. Adolescents whose thrill-seeking resulted in delinquency were thought to be engaging in normal adolescent behaviors, and they also were provided treatment. In contrast, courts that considered an adolescent’s behavior to be the product of rational choice identified him/her as a juvenile criminal, thus requiring punishment. The 1990s represent the seventh stage of delinquency history in the U.S. (Bartollas, 2003). Despite a more liberal shift in the presidential administration in the early 1990s, remnants of juvenile crime trends from the previous decade led to new policies consistent with the “get tough” orientation of the 1980s. The increase of harsh drug use (e.g., crack cocaine) in the 1980s also led to an increase in drug dealing and trafficking among adolescents; gangs sold these new drugs and firearms were employed for protection. The increased availability of firearms among gangs and drug-involved adolescents opened the way for a subsequent rise in firearm-related youth homicides in the 1980s and 1990s (CDC, 2001; Snyder & Sickmund, 1999). Policy makers in the 1990s responded with the development of curfew laws, parental
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responsibility laws, gang prevention programs, graduated sanctions for repeat offenders, juvenile boot camps, gun control legislation, increased access to juvenile records, increased efforts to relocate juvenile offenders to criminal court, and expanded court discretion to apply simultaneous juvenile and criminal punishments for delinquent offenders. The continuous decrease in adolescent arrests and self-report perpetration rates (see below) since the mid 1990s suggest that these policy efforts have perhaps contributed to the steady reduction in overall delinquency. The impact of these policies, however, is inconclusive (MMWR, 2003) as other factors (e.g., economic improvement) may have contributed to the decline (FBI, 2002a). In some cases, the impact of these laws may have led to increases in recorded offenses. For example, the surge in curfew statutes enacted in the mid 1990s (Bartollas, 2003) was followed by constitutional challenges and repeals several years later (ACLU, 1998). Arrest data (see Table 1) on curfew violation shows a steep rise from the early to mid 1990s, followed by recent decreases. This pattern is consistent with anti-curfew law arguments that claim such policies simply increase the influx of adolescents into the juvenile justice system (Sullivan, 2002). The manner in which society handles youthful offenders provides some insight into the way delinquency is defined and perceived. From the early colonial period to present day, two themes persist that influence the way researchers study delinquency. First, the notion that adolescents are physically and mentally different from adults14 provides the basis from which to study delinquency from an intrapersonal perspective that examines personal, biological, and psychological variations. Second, the early practice of handling first time delinquents within the family, followed by the courts’ policy of replacing the family (i.e., parens patriae), reflects the belief that delinquency could be influenced through external means (e.g., socialization). Politically motivated policy changes, however, illustrate the fluid nature of delinquency and the challenge that defining it presents. Nevertheless, by continuously revisiting our assumptions of what causes delinquency,
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Persons over 21 years of age.
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and by studying trends in associated behaviors, better attempts at understanding its etiology can be made. Recent U.S. Prevalence Rates of Delinquency Violent crime rates have decreased among adolescent and adult populations since the mid 1990s (CDC 2002; Snyder & Sickmund, 1999). Despite this positive trend, smaller decreases in lethal crime rates among adolescent populations, increased firearm use among adolescent subgroups, and self-reported adolescent victimization data suggest that the overall picture of violence among youth is not entirely positive. Table 2.1 shows delinquency arrest trends by gender between 1992 and 2001. The column labeled 1992-2001 indicates that, in 2001, aggravated assault arrests among females remained at 45% above 1992 levels. This pattern was consistent across several types of offenses including larceny, arson, other assaults, vandalism, weapons possession, prostitution, drug abuse, offenses against the family, driving under the influence, disorderly conduct, and curfew violations (see Appendix B for definitions). Compared to females, adolescent males have demonstrated an increase in arrests over this ten-year period for a handful of offense categories including other assaults, drug abuse, offenses against the family, driving under the influence, disorderly conduct, and curfew violations. With the exception of prostitution, however, males are arrested at over twice the rate of females for similar offenses. Table 2.2 illustrates adolescent self-reported delinquency and victimization trends among adolescent boys and girls between 1993, 1999, and 2001. Consistent with arrest trends noted in Table 2.1, current self-reported delinquent behaviors among females exceed rates from the previous decade. Specifically, females reported threatening someone with a weapon on school grounds and using marijuana, cocaine, and steroids more often in 2001 than in 1993. In contrast, males reported increased marijuana, cocaine, and steroid use within the same period. Similar to arrest patterns, males reported engaging in violent offenses more often than females across all three periods. This disproportionate pattern was consistent among drug offenses with the exception of alcohol and inhalant use in 2001. More recent data (Youth Online: Comprehensive Results, 2007) show that most self-reported delinquency, and drug use have continued
Theoretical Framework and Model Development
19
to decline. Table 2.3 lists the prevalence of male and female adolescent victimization and perpetration of delinquency, aggression, and drug use, from 1991 to 2005. The column labeled “2005-2001” contrasts the rates between the same years, with negative values indicating decreasing trends. While most reported perpetration and victimization have decreased or remained stable since 2001, weapon-carrying and physical fighting, including fighting which occurs at school, have increased slightly. The increase in fighting is largest among girls. In sum, short-term trends for delinquency arrests (1997 to 2001) and self-reported delinquency (1999 to 2001, 2001to 2005) indicate that delinquency, in general, is decreasing. However, rates for drug use and other forms of minor (e.g., curfew) and serious (e.g., weapons use) delinquency remain above the rates of a decade ago. Despite recent increases in weapons use and drug use among females, males perpetrate and are arrested for delinquent activities at more than twice the rate of their gender counterparts. Theoretical Perspectives on the Causes of Delinquency The current review follows the format described in Chapter 1 and illustrated in Figure 1.1. The review begins with a discussion of systems theory and its more recent applications to delinquency (Bartol & Bartol, 1989). The discussion extends to the integration of systems theory, Problem Behavior Theory, and Resiliency Theory. Specifically, in addition to Bronfenbrenner’s four subsystems (e.g., micro-, meso-, exo-, macro-) that explain human behavior, Jessor and Jessor’s (1977) personality, perceived environment, and behavior systems are combined with Bartol and Bartol’s (1989) infrasystem.15 The combination of the aforementioned theories forms the adapted systems theory framework that is used in this study, which now contains six subsystems: macro-, exo-, meso-, micro-, infra-, and behavior-systems. Within each subsystem are risk and protective factors that combine to yield negative or resilient outcomes. 15
The Jessors’ and the Bartols’ emphasize the importance of the individual system (i.e., infrasystem) for determining behavior; a component Bronfenbrenner de-emphasizes in favor of other systems that highlight process and interaction.
Table 2.1 Delinquency1 Arrest Trends by Gender Trends (percentage change)2
Number of Arrests 1992
Homicide
Aggravated Assault
Robbery
Forcible Rape
3
1997
4
2001
Gender
5
1992-1997
1997-2001
1992-2001
1,420
1,367
603
-3.7
-50.0
-51.9
Male
99
85
82
-14.1
-3.5
-17.2
Female
33,230
36,627
30,700
10.2
-16.2
-7.6
Male
6,325
9,378
9,226
48.3
-1.6
45.9
Female
17,317
22,274
14,709
28.6
-34.0
-15.1
Male
1,552
2,303
1,387
48.4
-39.8
-10.6
Female
3,042
3,211
2,777
5.6
-13.5
-8.7
Male
59
66
39
11.9
-40.9
-33.9
Female
(continued on next page)
Table 2.1 Delinquency1 Arrest Trends by Gender
(continued) Trends (percentage change)2
Number of Arrests 1992
Burglary
Larceny
Motor-Vehicle Theft
Arson
3
1997
4
2001
Gender
5
1992-1997
1997-2001
1992-2001
76,831
69,359
47,430
-9.7
-31.6
-38.3
Male
8,088
8,266
6,658
2.2
-19.5
-17.7
Female
187,832
194,956
126,684
3.8
-35.0
-32.6
Male
780
2,160
1,881
176.9
-12.9
141.2
Female
40,546
33,520
24,680
-17.3
-26.4
-39.1
Male
6,371
6,448
5,008
1.2
-22.3
-21.4
Female
5,094
5,406
4,812
6.1
-11.0
-5.5
Male
604
617
649
2.2
5.2
7.5
Female
(continued on next page)
Table 2.1 Delinquency1 Arrest Trends by Gender
(continued) Trends (percentage change)2
Number of Arrests 1992
3
1997
4
2001
Gender
5
1992-1997
1997-2001
1992-2001
Other Assaults
72,894
102,075
96,144
40.0
-5.8
31.9
Male
41,567 21,049
44,680 13,044
72.1 -9.7
7.5 -38.0
85.0 -44.0
Female
Stolen Property
24,148 23,310 2,796
3,141
2,284
12.3
-27.3
-18.3
Female
74,001
71,873
55,164
-2.9
-23.2
-25.5
Male
7,067
9,369
8,138
32.6
-13.1
15.2
Female
28,063
28,623
20,837
2.0
-27.2
-25.7
2,089
2,826
2,436
35.3
-13.8
16.6
Vandalism Weapons-carrying, Possessing
Male
Male Female
(continued on next page)
Table 2.1 Delinquency1 Arrest Trends by Gender
(continued) Trends (percentage change)2
Number of Arrests 1992
Prostitution
Drug Abuse Violations
Offenses Against Family Driving under the Influence
3
1997
4
2001
Gender
5
1992-1997
1997-2001
1992-2001
352
394
277
11.9
-29.7
-21.3
Male
349
513
582
47.0
13.5
66.8
Female
40,928
115,215
104,893
181.5
-9.0
156.3
Male
5,683
18,185
18,931
220.0
4.1
233.1
Female
1,662
3,653
3,319
119.8
-9.1
99.7
Male
780
2,160
1,881
176.9
-12.9
141.2
Female
7,330
9,208
9,503
25.6
3.2
29.6
1,170
1,843
2,069
57.5
12.3
76.8
Male Female
(continued on next page)
Table 2.1 Delinquency1 Arrest Trends by Gender
(continued)
Trends (percentage change)2
Number of Arrests
Disorderly Conduct
Vagrancy Curfew and loitering law violations Runaways 1
Gender
19923
19974
20015
1992-1997
1997-2001
1992-2001
49,831
88,111
65,977
76.8
-25.1
32.4
Male
14,554
30,037
27,833
106.4
-7.3
91.2
Female
1,830
1,703
1,227
-6.9
-28.0
-33.0
Male
319
306
305
-4.1
-0.3
-4.4
Female
31,198
86,882
61,111
178.5
-29.7
95.9
Male
12,139
37,663
27,492
210.3
-27.0
126.5
Female
44,719
47,831
32,279
7.0
-32.5
-27.8
Male
59,581
66,110
47,801
11.0
-27.7
-19.8
Female
Under 18 years of age. Formula = [(Year1 – Year2)/Year1] * (-100). The negative sign on –100 is to denote increasing and decreasing trends. 3 1992 Estimated Population 136,880, 883 with 7,135 reporting agencies (FBI, 2002). 4 1997 Estimated Population 156,946,206 with 7,571 reporting agencies (FBI, 2002). 5 2001 Estimated Population 167,076,444 with 7,571 reporting agencies (FBI, 2002). 2
Table 2.2 Self-Reported Problem Behavior and Victimization Trends by Gender Delinquent Behavior
1993 Gun-carrying6 Weapon-carrying
Trends (percentage change)2
Number of Incidents
6
7
Weapon-carrying Incidents
Weapon-carrying in School
3
1999
4
2001
Gender
5
1993-1999
1999-2001
1993-2001
1,156
696
682
-39.8
-2.0
-41.0
Male
141
61
91
-56.9
48.9
-35.8
Female
2,895
2,212
1,941
-23.6
-12.3
-33.0
Male
723
457
433
-36.8
-5.3
-40.1
Female
12,223
9,221
n.a.
-24.6
n.a.
n.a.
Male
2,820
1,728
n.a.
-38.7
n.a.
n.a.
Female
1,511
851
676
-43.7
-20.6
-55.3
Male
401
213
202
-46.8
-5.1
-49.5
Female
(continued on next page)
Table 2.2 Self-Reported Problem Behavior and Victimization Trends by Gender Delinquent Behavior
1993 Threaten/Use Weapon in School Physical Fighting 7
Physical Fighting Incidents In-School Fighting Lifetime Alcohol Use
Trends (percentage change)2
Number of Incidents 3
1999
4
2001
(continued) Gender
5
1993-1999
1999-2001
1993-2001
777
735
762
-5.4
3.6
-1.9
Male
424
442
454
4.1
2.7
6.9
Female
4,322
3,404
2,855
-21.2
-16.1
-33.9
Male
2,490
2,078
1,668
-16.5
-19.8
-33.0
Female
14,620
11,086
n.a.
-24.2
n.a.
n.a.
Male
7,611
5,177
n.a.
-32.0
n.a.
n.a.
Female
1,984
1,431
1,192
-27.9
-16.7
-39.9
Male
676
746
502
10.4
-32.7
-25.6
Female
6,829
6,220
5,206
-8.9
-16.3
-23.8
Male
6,354
6,220
5,435
-2.1
Female -12.6 -14.5 (continued on next page)
Table 2.2 Self-Reported Problem Behavior and Victimization Trends by Gender Delinquent Behavior
1993 Current Alcohol Use Lifetime Marijuana Use Current Marijuana Use Lifetime Cocaine Use Current Cocaine Use
Trends (percentage change)2
Number of Incidents 3
1999
4
2001
(continued) Gender
5
1993-1999
1999-2001
1993-2001
4,229
4,046
3,259
-4.3
-19.5
-22.9
Male
3,605
3,631
3,140
0.7
-13.5
-12.9
Female
3,106
3,945
3,080
27.0
-21.9
-0.8
Male
2,246
3,304
2,679
47.1
-18.9
19.3
Female
1,739
2,383
1,848
37.0
-22.4
6.3
Male
1,147
1,721
1,395
50.0
-18.9
21.7
Female
464
828
682
78.3
-17.6
46.9
Male
330
640
586
93.8
-8.4
77.7
Female
194
402
311
107.2
-22.6
60.3
Male
110
221
258
100.8
Female 16.9 134.8 (continued on next page)
Table 2.2 Self-Reported Problem Behavior and Victimization Trends by Gender Delinquent Behavior
1993 Lifetime Inhalant Use
n.a. n.a.
Current Inhalant Use
n.a. n.a.
Lifetime Steroid Use
Trends (percentage change)2
Number of Incidents 3
n.a.
1999
4
1,137 1,112 340 297 271
2001
(continued)
5
Gender
1993-1999
1999-2001
1993-2001
960
n.a.
-15.5
n.a.
Male
1,040
n.a.
-6.5
n.a.
Female
338
n.a.
-0.8
n.a.
Male
293
n.a.
-1.3
n.a.
Female
252
n.a.
-7.0
n.a.
Male
76.2
n.a.
Female
n.a.
99
174
n.a.
Current Steroid Use
262
402
397
53.7
-1.2
51.9
Male
167 410
272 344
77.7 -6.6
62.5 -16.0
188.7 -21.5
Female
Injured from Fight
94 439 212
213
202
0.5
-5.1
-4.6
Female
(continued on next page)
Male
Table 2.2 Self-Reported Problem Behavior and Victimization Trends by Gender Delinquent Behavior
1993 Hurt by Boy/Girlfriend Forced Sexual Intercourse Felt Unsafe at School 1
Trends (percentage change)2
Number of Incidents 3
1999
4
2001
(continued) Gender
5
1993-1999
1999-2001
1993-2001
n.a.
642
603
n.a.
-6.1
n.a.
Male
n.a.
708
684
n.a.
-3.4
n.a.
Female
n.a.
402
338
n.a.
-16.0
n.a.
Male
n.a.
952
719
n.a.
-24.5
n.a.
Female
363
371
384
2.3
3.5
5.8
Male
346
434
516
25.6
19.0
49.4
Female
Under 18 years of age. 2 Formula = [(Year1 – Year2)/Year1] * (-100). The negative sign on –100 is to denote increasing and decreasing trends. Note that number of incidents depend on the number of reporting agencies. Trends change is presented in relation to the number of incidents in Year1. 3 Total Sample Size: 16,296, Males: 51.8%, Females, 48.2%. 4 Total Sample Size: 15,349, Males: 50.4%, Females, 49.6%. 5 Total Sample Size: 13,601, Males: 48.7%, Females, 51.3%. 6 On one or more days preceding the survey. 7 Frequency in 30 day period per 100 students. n.a. - No data available.
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
Weapon-carrying
18.5 7.1 29.8
17.1 6.7 26.9
17.4 6.2 29.3
17.3 6 28.6
18.3 7 27.7
20 8.3 31.1
22.1 9.2 34.3
26.1 10.9 40.6
1.1 0.9 0.5
Total Girls Boys
Weapon-carrying in School
6.5 2.6 10.2
6.1 3.1 8.9
6.4 2.9 10.2
6.9 2.8 11
8.5 3.7 12.5
9.8 4.9 14.3
11.8 5.1 17.9
n.a.1 n.a. n.a.
0.1 -0.3 0
Total Girls Boys
Gun-carrying
5.4 0.9 9.9
6.1 1.6 10.2
5.7 1.3 10.3
4.9 0.8 9
5.9 1.5 9.6
7.6 2.5 12.3
7.9 1.8 13.7
n.a. n.a. n.a.
-0.3 -0.4 -0.4
Total Girls Boys
Physical Fighting
35.9 28.1 43.4
33 25.1 40.5
33.2 23.9 43.1
35.7 27.3 44
36.6 26 45.5
38.7 30.6 46.1
41.8 31.7 51.2
42.5 2.7 Total 34.4 4.2 Girls 50.2 0.3 Boys (continued on next page)
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender
(continued)
Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
In-School Fighting
13.6 8.8 18.2
12.8 8 17.1
12.5 7.2 18
14.2 9.8 18.5
14.8 8.6 20
15.5 9.5 21
16.2 8.6 23.5
n.a. n.a. n.a.
1.1 1.6 0.2
Total Girls Boys
Injured from Fighting
3.6 2.4 4.8
4.2 2.6 5.7
4 2.9 5.2
4 2.8 5.3
3.5 2.2 4.6
4.2 2.5 5.7
4 2.7 5.2
4.4 2.7 6
-0.4 -0.5 -0.4
Total Girls Boys
Had Property Damaged at School
29.8 28 31.4
29.8 26.2 33.1
32.9 29 36.1
34.9 27.9 41.4
32.7 28.1 37
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a.
-3.1 -1 -4.7
Total Girls Boys
Hurt by Boy/Girlfriend
9.2 9.3 9
8.9 8.8 8.9
9.5 9.8 9.1
8.8 9.3 8.3
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a.
-0.3 -0.5 -0.1
Total Girls Boys
(continued on next page)
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender
(continued)
Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
Forced Sexual Intercourse
7.5 10.8 4.2
9 11.9 6.1
7.7 10.3 5.1
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a.
-0.2 0.5 -0.9
Total Girls Boys
Felt Unsafe at School
6 6.3 5.7
5.4 5.3 5.5
6.6 7.4 5.8
5.2 5.7 4.8
4 3.9 4.1
4.5 4.3 4.7
4.4 4.4 4.3
n.a. n.a. n.a.
-0.6 -1.1 -0.1
Total Girls Boys
Threatened with Weapon at School
7.9 6.1 9.7
9.2 6.5 11.6
8.9 6.5 11.5
7.7 5.8 9.5
7.4 4 10.2
8.4 5.8 10.9
7.3 5.4 9.2
n.a. n.a. n.a.
-1 -0.4 -1.8
Total Girls Boys
Lifetime Alcohols Use
74.3 74.8 73.8
74.9 76.1 73.7
78.2 77.9 78.6
81 81.7 80.4
79.1 78.4 79.7
80.4 79.5 81.1
80.9 80.9 80.9
Total 81.6 -3.9 Girls 80.9 -3.1 Boys 82.3 -4.8 (continued on next page)
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender
(continued)
Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
Current Alcohol Use
43.3 42.8 43.8
44.9 45.8 43.8
47.1 45 49.2
50 47.7 52.3
50.8 47.8 53.3
51.6 49.9 53.2
48 45.9 50.1
50.8 48.8 52.7
-3.8 -2.2 -5.4
Total Girls Boys
Alcohol Use in School
4.3 3.3 5.3
5.2 4.2 6
4.9 3.8 6.1
4.9 3.6 6.1
5.6 3.6 7.2
6.3 5.3 7.2
5.2 4.2 6.2
n.a. n.a. n.a.
-0.6 -0.5 -0.8
Total Girls Boys
Lifetime Marijuana Use
38.4 35.9 40.9
40.2 37.6 42.7
42.4 38.4 46.5
47.2 43.4 51
47.1 42.9 50.7
42.4 39.4 45.3
32.8 28.6 36.8
31.3 29.8 32.8
-4 -2.5 -5.6
Total Girls Boys
Current Marijuana Use
20.2 18.2 22.1
22.4 19.3 25.1
23.9 20 27.9
26.7 22.6 30.8
26.2 21.4 30.2
25.3 22 28.4
17.7 14.6 20.6
Total 14.7 -3.7 Girls 12.5 -1.8 Boys 16.7 -5.8 (continued on next page)
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender
(continued)
Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
Marijuana Use in School
4.5 3 6
5.8 3.7 7.6
5.4 2.9 8
7.2 4.4 10.1
7 4.6 9
8.8 5.5 11.9
5.6 3.3 7.8
n.a. n.a. n.a.
-0.9 0.1 -2
Total Girls Boys
Lifetime Cocaine Use
7.6 6.8 8.4
8.7 7.7 9.5
9.4 8.4 10.3
9.5 8.4 10.7
8.2 7.2 9.1
7 5 8.8
4.9 4.2 5.5
5.9 4.4 7.3
-1.8 -1.6 -1.9
Total Girls Boys
Current Cocaine Use
3.4 2.8 4
4.1 3.5 4.6
4.2 3.7 4.7
4 2.9 5.2
3.3 2.4 4
3.1 1.8 4.3
1.9 1.4 2.3
1.7 1 2.4
-0.8 -0.9 -0.7
Total Girls Boys
Lifetime Inhalant Use
12.4 13.5 11.3
12.1 11.4 12.6
14.7 14.9 14.5
14.6 14.6 14.7
16 14.1 17.6
20.3 18.4 22.1
n.a. n.a. n.a.
n.a. n.a. n.a.
Total -2.3 Girls -1.4 Boys -3.2 (continued on next page)
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender
(continued)
Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
Lifetime Heroin Use
2.4 1.4 3.3
3.3 2 4.3
3.1 2.5 3.8
2.4 1.3 3.5
Lifetime Meth Use
9.8 9.2 10.5
9.1 8.4 9.9
Total Girls Boys
6.3 5.3 7.2
11.1 10.4 11.6
n.a. n.a. n.a.
n.a. n.a. n.a.
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
-3.6 -3.2 -4.2
Lifetime Ecstasy Use
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Total Girls Boys
7.6 6.8 8.3
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
-0.7 -1.1 -0.5
6.2 6 6.3
n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.
n.a. n.a. n.a.
Total Girls Boys
Lifetime Steroid Use
4 3.2 4.8
6.1 5.3 6.8
5 3.9 6
3.7 2.2 5.2
3.1 2 4.1
3.7 2.4 4.9
2.2 1.2 3.1
2.7 1.2 4.1
Total -1 Girls -0.7 Boys -1.2 (continued on next page)
Table 2.3 Trends in Percentage of Self-Reported Problem Behavior and Victimization by Gender
(continued)
Delinquent Behavior
2005
2003
2001
1999
1997
1995
1993
1991
2005-2001
Group
Lifetime IV Drug Use
2.1 1.1 3
3.2 2.5 3.8
2.3 1.6 3.1
1.8 0.7 2.8
2.1 1.5 2.6
2.1 1 3
n.a. n.a. n.a.
n.a. n.a. n.a.
-0.2 -0.5 -0.1
Total Girls Boys
Access to Drugs in School
25.4 21.8 28.8
28.7 25 31.9
28.5 22.7 34.6
30.2 25.7 34.7
31.7 24.7 37.4
32.1 24.8 38.8
24 19.1 28.5
n.a. n.a. n.a.
-3.1 -0.9 -5.8
Total Girls Boys
1
n.a. - No data available.
Theoretical Framework and Model Development
37
Following the development of the revised systems theory framework, personal, interpersonal, and contextual theories of delinquency are discussed with respect to their location in the new theoretical framework. Subsequent to the review of individual theories of delinquency, an integrative model of delinquency, proposed by Shoemaker (2000) is presented. In keeping with the systems theory framework, Shoemaker’s (2000) integrative model of delinquency also is discussed in relation to the revised systems theory framework. Shoemaker’s model is used as a summary of the delinquency literature since it is not based on any perspective but simply reflects the empirical evaluation of the individual theories. The purpose of this exercise is to delineate keyed constructs (i.e., explanatory factors) purported to be associated with delinquency and to determine their causal location in relation to delinquency within the new framework. The product of theoretical integration, at that stage, is a general conceptual model of relevant factors of problem behaviors arranged according to their location in a social systems framework. After developing a conceptual model that reflects the problem behavior literature in terms of interrelated systems, the next step is to identify the actual measures that will be used to represent the constructs in the final model for this study. To encourage interpretable results, pathways that are not logically consistent with the operationalized measures for this study are removed and noted. The final step in this review is a presentation and discussion of the hypothesized (testable) model. The discussion of the final hypothesized model describes the theoretical influences underlying the model and summarizes the hypothesized pathways that explain problem behaviors. Systems Theory/Ecological Approach The social and physical environment surrounding an individual can be described as an ecological map. Bronfenbrenner defines components of a person’s ecological map in terms of “ecological-environmental levels” or systems (Muuss, 2000). Bronfenbrenner suggests that a person’s development and behavior are determined by his/her perceptions of systems within that ecological map. A systems theory or ecological approach refers to the study of the manner in which systems, within an ecological map, interact to determine development
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and behavior. Social systems refer to systems that influence development and behavior that are interpersonal in nature (e.g., parents, peers). Finally, a social systems framework (Bartol & Bartol, 1989) can best be understood as an overarching theory regarding the manner in which social systems combine to determine development and behavior. A social systems framework is used to guide the development of the delinquency model in this study. Although the current study is cross-sectional in nature, a social systems approach allows for the study of human behavior in the context of multiple, simultaneous forces across time. The simultaneous forces may operate at various conceptual levels (e.g., personal, interpersonal, contextual). Studies that examine how a structural variable (e.g., socioeconomic status) interacts with a personality variable (e.g., impulsivity) to result in delinquent behaviors are consistent with a systems approach (Lynam, Caspi, Moffit, Wikstroem, Loeber, & Novak, 2000). A social systems framework also facilitates a multi-disciplinary understanding of human behavior. In other words, by incorporating a variety of factors at different conceptual levels, researchers can attempt to integrate alternative and complementary theories of delinquency under a single theoretical umbrella. Bronfenbrenner’s (1979) ecological model of human development (see Figure 2.2) illustrates his social systems framework. The multilevel model illustrates the theorized influences and processes that combine to determine human development. Since delinquency is an age-related developmental phenomenon, Bronfenbrenner’s work is relevant to etiological and preventive studies of delinquency. Specifically, Bronfenbrenner’s propositions regarding the ways in which the social environment influences, and is changed by a person’s development, provide a framework within which to explore how that same social environment determines a person’s behavior. The various constructs and processes that impact development are located within various systems.16 Bronfenbrenner’s model is divided into four systems (i.e., micro-, meso, exo-, macrosystem) that become increasingly important, or relevant, to the individual as he/she develops. The increasing breadth of each system is denoted by its 16
These systems may also be referred to as ecological-environmental levels or conceptual levels.
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larger diameter in Figure 2.1. Bronfenbrenner later (Bronfenbrenner, 1989; Muuss, 2000) incorporated the temporal component as a fifth chrono-system, which is represented by the height of the cylinder in Figure 2.1. Bartol and Bartol (1989) highlight the importance of individual differences in the ecological model and incorporate an additional system, the infrasystem, discussed later. Due to its breadth, Bronfenbrenner’s model of human development not only provides a conceptual framework to study adolescent behavior but also serves as a theoretical grid where models of more limited scope can be mapped. The components of this ecological model and their relationship to problem behavior research are discussed below.
Figure 2.1 Ecological Model of Human Development (Bronfenbrenner, 1979)
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Microsystem The microsystem is the social building block of Bronfenbrenner’s model. A microsystem refers to patterns of activities, roles, or personal relationships an individual directly experiences with significant others including, peers, family members, neighbors, teachers, church groups, summer camps, or work settings (Bartol & Bartol, 1989; Bronfenbrenner, 1993; Muuss, 2000). Just as an individual develops, his/her microsystems, the persons that comprise them, and the relevance of the microsystem to the individual, also change. For a toddler, the only microsystem may be his/her parents and it is very relevant. For adolescents, the number of microsystems often expands to include peers, athletic teams, and clubs. Although peers become increasingly relevant throughout adolescence, the parental microsystem also remains an important aspect of adolescent lives in different domains. Steinberg (2001) and others (e.g., Brown, 1990; Bartollas, 2003; Gecas & Seff; 1990; Sebald, 1986) have noted that, during adolescence, peers are important influences on decisions regarding fashion, music, and engaging in risky behaviors whereas parents remain important sources of moral and religious guidance. Two key aspects of microsystems are: a) that they are directly experienced by the individual and b) he/she deems those experiences to be relevant. Experience is a crucial component of Bronfenbrenner’s theory and represents the underlying social psychological and ecological features of his theory. Bronfenbrenner developed his concept of the perceived environment from the contributions of Lewin’s (1935) field theory. According to Lewin (Bronfenbrenner, 1979), behavior (B) can be defined algebraically as the function (f) of the person (P) and the environment (E): B = f(PE). The relationship between the person and the environment is assumed to be interactive (Muuss, 2000). Studies that focus solely on the person (P) or the environment (E) provide only limited information on external and internal variables that influence behavior. Studies that focus on the interrelationship between the person and the environment provide information about the processes between the two variables that result in behavior. The interaction (i.e., location) of two constructs is defined as an ecological niche.
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One of Bronfenbrenner’s (1979) modifications to Lewin’s field theory was the exchange of behavior (B) for development (D). Hence, human development was now the function (i.e., interaction) between the person and the perceived environment. The perceived environment, according to Lewin, was more relevant than the physical environment for the purposes of explaining behavior. For example, an adolescent’s appraisal of parental monitoring practices (and the expected consequences of ‘getting caught’) is more relevant to participating in delinquent behaviors than the parents’ actual monitoring. Objective parental monitoring is effective to the extent that parents have the time and resources to actually inquire and be witness to an adolescent’s behavior. In contrast, the perception that one is being monitored can be effective at constraining problem behavior at all times. In sum, an adolescent’s perception of herself and her parental microsystem determines the effectiveness of that microsystem on her behavior. Alluding to the nature of microsystems, Bronfenbrenner (1979) notes that: …a critical term in the definition of a microsystem is experienced. The term is used to indicate that the scientifically relevant features of any environment include not only the objective properties but also the way in which these properties are perceived by the persons in that environment…Very few of the external influences significantly affecting human behavior and development can be described solely in term of objective physical conditions and events; the aspects of environment that are most powerful in shaping the course of psychological growth are overwhelmingly those that have meaning to the person in a given situation (p. 22). Mesosystem Bronfenbrenner’s focus on the perceived environment is not limited to a single microsystem but extends to the relationships between them. The mesosystem represents the interrelationships between microsystems (Bartol & Bartol, 1989; Bronfenbrenner, 1979; Muuss, 2000). Methodologically, models at the mesosystem level examine the correlation or association between two microsystems (e.g., parents,
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peer group). Mesosystem models are critical to the study of adolescent behavior since this stage is marked by new, complex roles for the adolescent that involve additional microsystems. As noted earlier, toddlers may only have one or two microsystems: parents and playmates. On the other hand, adolescents may have parents, peers, coworkers, athletic teams, and clubs with which they interact. According to Bronfenbrenner (1979), developmental research has predominately focused on individual microsystems to explain behavior while ignoring their interactions (Bartol & Bartol, 1989; Muuss, 2000). A study by Bischof, Stith, and Whitney (1995) exploring family environment differences between juvenile sex offenders and violent and non-violent delinquents is an example of research limited to one microsystem; the interaction between the offender and his/her family environment. Recent work by Scaramella, Conger, Spoth, and Simons (2002), and work by Griffin et al. (1999), however, illustrate efforts to incorporate the interaction of multiple microsystems (e.g., peer delinquency/influence and parental monitoring/influence) into explanatory models of adolescent delinquency and violent behaviors. Mesosystems can be sources of conflict for adolescents when they endorse negative behaviors or divergent values (Muuss, 2000). Whereas the parental microsystem may endorse moral values that discourage delinquent behaviors (i.e., theft and vandalism), the peer microsystem may encourage these behaviors (often as a way for adolescents to establish and maintain social status within a peer network). The relevance, quality, and nature of each microsystem may dictate which microsystem exhibits the most influence on adolescent behavior. For example, parental monitoring may ultimately be more effective at determining adolescent delinquency if it plays a role in determining (i.e., mediating) peer group associates of the adolescent (Scaramella et al., 2002). Overall, the mesosystem has two important characteristics: (a) it involves the interrelationship of microsystems, and (b) these microsystems include experiences and interactions directly experienced by the individual. Exosystem The exosystem refers to the larger community in which the individual resides. Unlike the micro- and mesosystems, the exosystem incorporates the indirect influence of distal variables on the individual.
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Exosystem variables, in general, do not impact the individual directly but affect those who come in contact with the adolescent. In other words, the exosystem represents processes that are mediated by the impact microsystems have on the individual (Bronfenbrenner, 1979). Examples of exosystems include parental work force demands (e.g., long work hours), local government decisions (e.g., curfew laws), and local transportation options (e.g., that impact drinking and driving rates). As an individual develops, exosystems may become part of his/her mesosystem (Muuss, 2000). An 18 year-old adolescent who becomes involved in local politics that affect him/her directly has incorporated (into his mesosystem) what was once a distal factor (local politics) that was mediated by his parental microsystem. Exosystems also can have negative effects on delinquency when, for example, decisions by employers extend parental working hours (distal) and increase the amount of unsupervised time an adolescent spends during school days (i.e., the parental microsystem). Alternatively, local government decisions to enforce truancy and curfew laws, fund afterschool programs, and cap employment age can influence delinquency by impacting the way adolescents spend their time during and after school, and how they interact with their school-, club-, work-, and peerrelated microsystems. Macrosystem The macrosystem represents a “societal blueprint” (Muuss, 2000, p. 330) denoting the patterns of micro-, meso-, and exosystems that define a particular culture’s beliefs, values, opportunities, and social interchange (Bronfenbrenner, 1993). Unlike the exosystem which may have some direct influence on the individual, macrosystem forces have no direct influence on individuals. Examples of macrosystem level influences include societal views on women’s roles, definitions of beauty, and family roles, as well as economic conditions and political systems that impact exosystem and microsystem variables. Alone, macrosystem variables merely suggest how individuals and groups in a society may behave (Bartol & Bartol, 1989), but their influence on behavior may be mitigated by exosystem-level influences and other sub-level factors (Muuss, 2000). For example, in a study of peer and parental influence in a sample of Russian and American adolescent males, Bronfenbrenner (1967) found that, compared to
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American boys, Russian boys experienced less conflict between peer and parental influences. While living under different (i.e., communist ) macrosystem at the time, Russian peer groups and family members adhered to core societal values with less divergence than the American peer and family groups. This example illustrates the potentially strong influence of macro level variables; although, other studies have shown how individual and family level factors may offset the negative impact of other macro level forces (e.g., economic depression).
Figure 2.2 Bronfenbrenner’s Ecological Model of Human Development (Muuss, 2000)
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In Elder and Caspi’s (1988) study of children of the Great Depression, adolescents whose family’s economic status was financially affected by the depression were affected differently than younger children. Specifically, adolescent males were more likely to enter the workforce and develop a sense of agency; leading many to become productive adults. Interestingly, preschool children experienced the opposite results with less school and work success along with emotional and social problems (Muuss, 2000). Adolescent females also did not benefit from the circumstances, as did males, presumably due to fewer opportunities for employment. Infrasystem The innermost circle of Figure 2.1 represents the individual in Bronfenbrenner’s (1979) model. Although Bronfenbrenner acknowledges individual differences, he argues that the building block of human behavior is the interaction between the individual and surrounding forces. Bronfenbrenner reasons that while demographic (e.g. age, gender, race) variables are important for targeting interventions for particular populations, they represent constructs and structures that are not easily changed. In other words, the opportunity to effect change with an intervention lies within the process of human interaction (Muuss, 2000), not static qualities. While Bronfenbrenner's argument about the lack of utility and malleability of demographic factors is well understood, the importance of personality factors in affecting delinquency should not be discounted. To address Bronfenbrenner’s “omission,” Bartol and Bartol (1989) have suggested adding a fifth level called the infrasystem that would “treat” individual factors as a system onto itself. Specifically, the Bartols suggest a system “where personal constructs, beliefs, or schema interact with temperament and genes to form a personality (p. 122).” Moreover, the Bartols posit that whereas macro-, exo-, meso-, and micro-level factors provide individuals with information that influences their behaviors, the manner in which that information is processed is ultimately determined by the characteristics of the individual (Bartol & Bartol, 1989). Three aspects of Jessor and Jessor’s (1977) Problem Behavior Theory (PBT) complement systems theory and provide the conceptual clarity necessary to translate from an ecological framework of human development to an ecological
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framework that explains human behavior. However, before proceeding to integrate these two theories, a brief review of the major components of PBT is warranted. Problem Behavior Theory Problem behavior theory is a social-psychological theory that examines how personality and perceived environments combine to explain prosocial and problem behaviors. Figure 2.3 illustrates a simplified version of the PBT model. Demography and social structure are antecedent factors (e.g., family structure and socioeconomic status) that have a distal bearing on the decision to engage in prosocial or problem behaviors. The intensity of the association is depicted by the style of the lines in Figure 2.3, where solid lines represent stronger associations between systems. Socialization is another distal variable that includes such factors as family values, peer influence, and media influence. The personality system consists of three structures: motivational-instigation, personal-belief, and personal-control. Motivational-instigation contains seven variables that measure a person’s value and expectations of achievement, independence, and affection. The personal-belief structure contains variables that “constrain against the instigations to engage in problem behavior that derive from variables in the preceding motivational-instigation structure” (Jessor & Jessor, 1977, p. 20). Variables in the personal-belief structure include social criticism, alienation, self-esteem, and internal-external locus of control. Personal-control structures serve the same constraining purposes as personal-belief variables. However, personal-control structures are considered to be more directly tied to problem behaviors. These variables include attitudinal tolerance for deviance, religiosity, and an index measure of the reason for engaging in prosocial and problem behaviors. Overall, the personal-belief system is a combination of motivations and constraining forces against behavior, whether that behavior is prosocial or problematic. Whereas the Jessors’ use a combination of indicators to establish motivation and constraint, other researchers have used one or two constructs (e.g., self-esteem and locus of control) with mixed results. Despite establishing direct associations between personality variables such as self-esteem and locus of control (Downs & Rose, 1991; Jessor & Jessor, 1977; Parrott & Strongman,
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1984), support for their mediating properties is less conclusive (Peiser & Heaven, 1996; Shaw & Scott, 1991). The fourth component of PBT is the perceived environment system, consisting of the distal structure and the proximal structure. The distal structure contains six variables that measure whether an adolescent is oriented toward, or influenced by, the family or peer group (whereby peer influence is viewed as conducive to problem behaviors). Variables that form the distal structure include parental support, parental controls, friends’ control, friends’ support, parentfriend compatibility, and parent-friend influence. The combination of variables is considered distal as “they do not directly or necessarily implicate problem behavior but can be linked to its occurrence by reliance on theory and the mediation of other variables (Jessor & Jessor, 1977, p. 27).” In contrast, the proximal structure measures whether support for problem behavior derives from parental approval, peer approval, or peer modeling of such behavior. According to the Jessors, the proximal environment is directly tied to the occurrence of problem behavior. The last component of the PBT is the behavior system that is divided into the problem behavior structure and the conventional behavior structure. The problem behavior structure is comprised of a set of actions that, when performed by adolescents, elicits a response from adults to control (i.e., prevent) future occurrences. Examples of such behaviors include, but are not limited to, drug use, violence, and illicit sexual activity. Conventional behaviors refer to actions that are normative or socially (and developmentally) expected, such as academic achievement. As suggested earlier, components of PBT are congruent with systems theory and, in the case of the behavior system, extend Bronfenbrenner’s (1979) ecological model. Figure 2.3 illustrates how components of PBT can be interpreted as different ecological systems as defined by Bronfenbrenner. For example, the personality system in PBT can be subsumed under the Bartols’ infrasystem as they share basic assumptions about the perceived environment and the constructs of personality. Two defining features of the Bartols’ infrasystem include the combination of genes, beliefs, and schema to form personality, as well as the mediating role personality then plays between other ecological systems and behavior. Consistent with these two features, the personality system of the PBT also is derived from a combination of factors (i.e., motivation, beliefs,
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and controls) that mitigate participation in problem behaviors. Interaction between the personality system and the perceived environment (e.g., parental support) and socialization (e.g., peer influence) represent separate microsystems. The relationship between these two microsystems forms a mesosystem. Consequently, the relationships between social structure and socialization, and between social structure and the perceived environment, represent an exosystem. PBT and systems theory also share underlying assumptions about the perceived environment. Figure 2.3 Conceptual Model of Problem Behaviors (Jessor & Jessor, 1977)
The perceived environment system in PBT refers to “the environment that…psychologists such as Lewin (1935) …referred to by the concept of the ‘life space’” (Jessor & Jessor, p. 27), wherein what is perceived is more relevant than the physical or “demographic” environment in determining behavior. As noted earlier, the perceived environment is crucial to Bronfenbrenner’s ecological system and considered just as important (if not more) than the physical environment in determining behavior. In addition to sharing many aspects of systems theory, PBT provides some conceptual clarity to the systems theory approach.
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Bronfenbrenner’s (1979) modification of Lewin’s definition of behavior from B = f(PE) to one of human development where D = f(PE) implies that the outcome variable is a person’s developmental status. Specifically, Bronfenbrenner’s entire ecological map represents human development depicted as the product of the individual/adolescent and the perceived environment (i.e., the microsystem, mesosystem, exosystem, the macrosystem). Unfortunately, this conceptual and pictorial model presents a challenge when one (re)applies it to the explanation of human behavior. If, as the Bartols and Jessors have suggested, one incorporates the individual/adolescent as an infrasystem, how can one depict the influence of the infrasystem on behavior? Jessor and Jessor (1977) resolve this issue by conceptualizing behavior as a system unto itself. Incorporating the behavior system into Bronfenbrenner’s ecological model allows one to redefine behavior (BhS) as the interaction between the infrasystem (I) and the perceived environment: BhS=f(I)*(MicroMesoExoMacro), where the perceived environment is the product of Micro = microsystem, Meso = mesosystem, Exo = exosystem, and Macro = macrosystem. The equation discussed above represents the adapted systems theory framework. The graphical representation of this adapted framework is shown in Figure 2.4 below. The adapted framework is similar to Bronfenbrenners’ original framework with two exceptions. Behaviors are now subsumed under the behavior system and comprise both prosocial and antisocial behaviors. Individual characteristics are now subsumed under the infrasystem. More importantly, the infrasystem is purported to mediate the relationship between macro-, exo-, meso, and microsystems and behavior. The adapted framework provides a template from which to develop the model of adolescent problem behaviors. Constructs or measures used in a model must be mapped onto to the system or ecological level where they are purported to operate (e.g., infra-system, macro-system). This requirement serves to maintain conceptual clarity regarding what a model is intended to explain. Specifically, the framework separates psychological/biological outcomes (infra-system) from behavioral outcome (behavior-system). This distinction is crucial when the purpose of a research study or program evaluation is not only to explain psychological (e.g., personality) outcomes but to explain behavioral outcomes (e.g., problem behaviors). Before proceeding to develop a
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model of adolescent problem behaviors, however, a discussion of the role resiliency plays in the current framework is necessary. Figure 2.4 Revised Ecological Framework for Exploring Problem Behaviors
Resiliency Theory As noted earlier, systems theory and PBT allow for the study of risk and protective factors that inhibit and promote problem behaviors. Zimmerman and Arunkumar (1994) define resiliency as “fending off maladaptive responses to risk and their potential negative consequences” (p. 2), whereas Gamerzy and Masten (1991) view resilience as “a process of, capacity for, or the outcome of, successful adaptation despite challenging and threatening circumstances” (p. 459). Thus, resiliency research focuses on delineating protective factors and processes that foster normative behaviors or minimize negative
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outcomes “in the presence of risk” (Bronfenbrenner, 1979; Zimmerman & Arunkumar, 1994). From a longitudinal perspective, resiliency theory explores how negative experiences help “steel” or prepare an individual to handle future risks and challenges (Garmezy, Masten, & Tellegen, 1984; Zimmerman & Arunkumar, 1994). Staudiger, Marsiske, and Baltes (1993) view resiliency as “the maintenance of healthy development despite the presence of threat” (p.3). Research on resiliency was inspired by the identification of a multitude of risk factors to problem behaviors (Dryfoos, 1990; Hawkins, Catalano, & Miller, 1992; Newcomb & Felix-Ortiz, 1992). Armed with this knowledge, researchers began wondering why some children exposed to particular risk factors did not experience negative outcomes (Rutter, 1979; Werner, 1993; Werner & Smith, 1992). For example, Werner (1993; Werner & Smith, 1992) found that, among a cohort of 201 Kauai children, over third grew up to be well-adjusted adults (e.g. caring, confident), despite being born under high-risk conditions (e.g., poverty, family, conflict, prenatal stress). Since the late 1970s, resiliency research has focused on the identification of protective factors, as well as the processes under which they operate. Bronfenbrenner (1979) calls for research that moves away from deficit models that seek to explain negative outcomes in terms of some deficiency within the individual. Instead, he proposes the use of an asset-model that views each individual as possessing particular qualities, traits, or skill sets that protect him/her from adverse conditions. Bronfenbrenner’s call has been echoed in the developmental (Zaslow & Takanishi, 1993) and delinquency (Pollard, Hawkins, & Arthur, 1999) literatures. Resiliency theory complements existing delinquency (and problem behavior) theories. For example, the prosocial path within the social development model of delinquency (O’Donnell, Hawkins, & Abott, 1995) includes several causally linked protective factors that inhibit participation in delinquency (e.g., involvement in prosocial activities, attachment, and commitment to prosocial others). Although resiliency research lends itself to longitudinal designs examining mediation, or cross-sectional and longitudinal designs of moderation (Zimmerman & Arunkumar, 1994), cross-sectional mediated designs (Griffin et al., 1999) remain useful and appropriate. When guided by theory, cross-sectional examinations can be useful for establishing initial relationships between risk and protective factors and
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delinquency. They also can serve as theoretically-guided precursory steps to subsequent longitudinal analyses. An example of a crosssectional design of resiliency and delinquency involves establishing a relationship between a risk factor (R1) and delinquency (D1), then testing whether a protective factor (P1) mediates the relationship between the risk factor and delinquency. If theory and logic support the direction of the mediated path, the above analysis would imply, but not confirm, that the risk factor mediated the relationship. This information can later be used to examine whether exposure to risk at Time 1 (R1) is associated with delinquency at Time 2 (D2), which is mediated by the role of a protective factor at Time 2 (P2). Moreover, cross-sectional studies using structural models can provide information on potential complex relationships involving a larger number of variables; information that may be useful in guiding future research. Summary of Adapted Ecological Framework Up to this point, this review has focused on establishing a framework to guide the development of an ecological model of problem behaviors. Bronfenbrenner’s (1979) systems theory framework, represented by his ecological model of human development, was adapted to include an infrasystem (Bartol & Bartol, 1989) and a behavior system (Jessor & Jessor, 1977). These steps correspond with the first row of Figure 1.1. The purpose of this effort was to arrive at an adapted framework that distinguishes between personal (i.e., infra-system) and behavioral outcomes. Having adapted this framework (see Figure 2.4), the discussion now turns to a review of personal theories of delinquency. Personal Theories and Perspectives Personal theories of delinquency assume that biological/ physiological (Dugdale, 1877; Goddard, 1913; Glueck & Glueck, 1950; LombrosoGuerrero, 1911) and psychological characteristics (Eysenck, 1977; Hirschi & Hindelang, 1977; Wilson & Herrstein, 1985) are the sole or primary determinants of whether a person will engage in delinquent behaviors. Biological/physiological theories of delinquency have obtained limited empirical support (Bartollas, 2003; Shoemaker, 2000) and provide greater explanatory power only in combination with other non-personal factors (Shoemaker, 2000). Lombroso’s (LombrosoGuerrero, 1911) claims that the inborn criminal element in humans is
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caused by the recessive traits of the atavistic man, as evidenced by physical/phrenological differences in skull size, have since been refuted (Goring, 1972). Similarly, support for a link between ‘mesomorphic’ (i.e., muscular) body type and delinquency (Cortes & Gatti, 1972; Glueck & Glueck, 1950), has been weak (Epps & Parnell, 1952; West & Farrington, 1973), contradictory (McCandless, Persons, & Roberts, 1972; Wadsworth, 1979), or lacking in methodological controls (Gibbens, 1963). Genealogical studies linking delinquency to parentally-transmitted ‘feeblemindedness’ and low IQ (Dugdale, 1877; Goddard, 1913; Healy & Bonner, 1916) also have been met with methodological challenges (Sutherland & Cressey, 1978) and contradictory findings (Slawson, 1926). However, Hirschi and Hindelang’s (1977) reexamination of data on IQ and delinquency have led those authors to conclude that IQ is a more relevant predictor of delinquency than social class and race, and that this is not a result of a race/class method bias, as suggested by Sutherland (Sutherland & Cressey, 1978). In light of Hirschi and Hindelang’s (1977) conclusions, and additional research linking low IQ to delinquency (Gordon, 1986; Kirkegaard-Sorensen & Mednick, 1977; West & Farrington, 1973), support for this association is moderately strong. Twin-based genetic studies also have revealed consistent genetic links to delinquent behavior. A study (Christiansen, 1977) of 3,586 Danish twins born between 1870 and 1920 showed that identical twins were more likely to share similar criminal histories (.50 concordance) than fraternal twins (.21 concordance). In a larger study of 13,000 Danish adoptees between 1924 and 1947, Christensen (1977) found that the percentage of boys with a criminal conviction was highest among those with biological and adoptive parents with a criminal conviction (24.5%), followed by those with only criminal biological parents (20%), only those with criminal adoptive parents (14.7%), and those with no criminal parents (13.5%). Although these findings suggest a strong genetic influence, they did not control for possible differences caused by the unshared environment (Bartol & Bartol, 1989; Bartollas, 2003). Eysenck’s (1977, 1983, and 1984) work on the link between the autonomic nervous system (ANS) and delinquency provides a bridge between biological and psychological theories of delinquency (and interpersonal functioning). Eysenck’s theory of criminality purports
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that delinquency is more prevalent among children whose insensitivity to changes in their ANS diminish their ability to be conditioned to engage in normative behavior. According to Eysenck (1977), children with an extroverted personality are impulsive, aggressive, and temperamental and thus more likely to engage in antisocial or delinquent behavior. Although some studies have found limited support (Miller & Lynam, 2001; Passingham, 1972), others have argued that the higher rates of self-reported delinquency among extroverts may be a function of that personality types' tendency to disclose socially unpopular behavior (Farrington, Biron, & LeBlanc, 1982). More recent work by Moffit (1993) and colleagues (Moffit, Lynam, & Silva, 1994) on low neuropsychological functioning and the early onset of delinquency provide a more promising avenue of research linking physiology and delinquency (Bartollas, 2003). Nevertheless, many of Eysenck’s assertions about the personality characteristics of delinquents do have some empirical support. Conger and Miller (1966) report that compared to non-delinquent males, delinquent boys were more emotionally unstable, impulsive, irritable, and unhappy. Zuckerman (1989) also has found that delinquent offenders are irresponsible, impulsive, and aggressive. Cross-national studies of American and New Zealand delinquents (Caspi, Moffitt, Silva, Stouthamer-Loeber, Krueger, & Schmutte, 1994) have found that offenders rate high on negative emotionality and lack of constraint, regardless of age, gender, and race. Caspi et al. suggest that in a context of low constraint, negative emotions are easily translated into anti-social or delinquent behaviors. In a meta-analysis of personality variables and antisocial behavior (ASB), Miller and Lynam (2001) found ASB was negatively associated with constraint (r = -.26, p < .001) and positively associated with hostility, psychoticism (r = .39, p < .001), negative emotionality (r = .27, p < .001), and thrill-seeking (r = .39, p < .001). John, Caspi, Robins, Moffit, and Stouthamer-Loeber (1994) reported that high levels of delinquency were related to similar levels of the sensation-seeking component of extroversion among 12 to 13 year old boys. Cloninger (1987) also has associated thrill-seeking with delinquency while (Tang, Wong, & Schwarzer, 1996) found a positive association between drug use and sensation-seeking. In addition, the Jessors (1977) and others (Jessor, Van Den Bos, Vanderryn, Costa, et al., 1995) have found an association between problem behaviors (i.e., delinquency and drug use) and the personality
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variables of external locus of control and low self-esteem. This finding also is supported by Peiser and Heaven’s (1996) work showing a relationship between low self-esteem, external locus of control, and delinquency. More recently the low-self esteem/problem behavior hypothesis was been questioned by Baumeister and others (Baumeister, Bushman, & Campbell, 2000; Baumeister, Campbell, Krueger, & Vohs, 2005; Baumeister & Smart, 1996; Bushman & Baumeister, 1998) who found a link between high self esteem and aggression. However, a recent study on adolescent and college students in the U.S. and New Zealand (Donnellan & et al., 2005) has found additional evidence linking low self-esteem to aggression, antisocial behavior, and delinquency. Although, these findings indicate associations between personal variables and problem behaviors, less is known about the ways in which these variables interact with other influences to determine problem behaviors. Social Structural Theories Social disorganization theory (Shaw & Makay, 1942, 1949) suggests that a socially disorganized community (e.g., undergoing rapid economic growth, decline, or repopulation) will experience diminished capacity to exert social control (i.e., the transfer of traditional or prosocial value) over its core population (i.e., the neighborhood or family unit). The vacuum created by the diminished social control within a community is supplanted by an alternate subculture. Within this subculture, delinquency develops as an alternate vehicle of socialization. Shaw and McKay (1942) developed their theory of social disorganization based on the social ecological (i.e., environmental) studies of communities around the Chicago area in the 1930s. The authors found that delinquency was highest in urban and industrial areas (with high heterogeneity, mobility, and poverty) and lowest in suburban and residential areas. Shaw and McKay suggest that economic fluctuations that affect the development and maintenance of business and the community infrastructure also impact the development of housing and, consequently, the likelihood that residents stay or leave the community. Rapid changes in the local population and unmaintained infrastructure make it difficult for local residents to integrate traditional social values. The lack of economic opportunity in such contexts allows criminal (and delinquent) activities to become
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alternate means of subsistence. The criminal/delinquent activity then becomes the socialization mechanism that leads to further delinquent activity. Although social disorganization theory has influenced other structural theorists (e.g., Merton, 1957), it is not without its critics. Arnold and Brundghart (1983) claim that social disorganization theory is not a true theory of delinquency. According to the authors, the presence of social disorganization is not a necessary or sufficient condition for delinquency to occur. Whereas Shaw and McKay (1949) contend that economic conditions foster delinquency by reducing the impact of a community’s social control, Miller (1958) argues that qualities inherent in the lowerclass’ culture promote delinquency. Specifically, Miller suggests that because members of the low- and middle-class do not enjoy the privileges of affluence, they feel a lack of control and autonomy over their lives. Members of the lower class compensate for this lack of autonomy through specific same-sex cultural interactions that demonstrate their toughness. Miller’s theory is more representative of the gang subcultures whose values are in opposition to the larger cultural values and serve to propagate the existence of the gang and its activities. Hirschi (1969), however, has found few differences in adolescent social bonding across levels of socioeconomic status. In contrast to Miller’s assertions about the link between lowerclass culture and delinquency, Cohen (1965), Merton (1957), and Cloward and Ohlin (1955) argue that lower- and middle-class members have values and goals (e.g., achieve wealth and prosperity) similar to the upper-class. According to Cohen’s (1965) theory of delinquent subcultures, however, when not provided with the means to achieve those goals, lower-class members become frustrated, suffer low selfesteem, and respond by engaging in delinquent behaviors that go against pro-social middle class values. In contrast, Merton’s strain theory (1957) suggests that lower-class members may respond in one of five ways: conformity, whereby adolescents accept societal goals and the means society provides to achieve them (e.g., become wealthy by achieving high standards at school and at work); innovation, whereby adolescents accept socially prescribed goals but develop alternate (e.g., delinquent) means of obtaining them (e.g., become wealthy by cheating at school and stealing at work); ritualism, whereby adolescents no longer value the socially prescribed goals but continue to follow the socially prescribed rituals to achieve them in some limited fashion (e.g.,
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attending school with no interest in achievement); retreatism, whereby adolescents no longer value socially prescribed goals or ascribe to the means to achieve them (e.g., living on the streets with no interest in becoming self-sufficient or getting a job to obtain independence); and rebellion, whereby adolescents reject socially prescribed goals and substitute them with their own (e.g., becoming a gang-member, abusing drugs). Cloward and Ohlin’s (1955) opportunity theory derives from Merton’s typology of social modes of adaptation. However, these authors posit that, upon experiencing strain to achieve desired goals, adolescents may proceed via three pathways: engage in innovation to achieve economic success through a criminal subculture (e.g., gang); engage in innovation through the violence and conflict subculture of a gang that values and respects such behavior; or engage in retreatism through drug use. Although Palmore and Hammond (1964) and Spergel (1964) have found support for Cloward and Ohlin’s (1955) three pathways, other studies have obtained mixed results (Short, Rivera, & Tennyson, 1965). Moreover, some researchers have questioned several assumptions of these theoretical perspectives (Kornhouser, 1978; Vold & Bernard, 1986), including whether gang members follow such strict decision making processes (Nettler, 1984). Social Reaction Theories Social reaction theories, or labeling theories, argue that delinquents are created through the social process of labeling. Adolescents who are apprehended for a minor transgression and labeled ‘delinquent’ come to accept that label and act in accordance with that label. Lemert (1951) describes this process in terms of primary and secondary deviation. Initial factors lead the adolescent to engage in primary deviation (initial offense), which, in turn, elicit the stigmatizing label of deviant (delinquent). The stigmatizing label becomes incorporated into the adolescent’s self-image, leading to secondary deviation (subsequent offense). According to Lemert, this process may undergo several changes as the stigmatized label is forged further into the self-image. Furthermore, Becker (1963) suggests that what starts as a social process culminates in structural change. That is, the process of labeling and stigmatizing, and of subsequent internalization of the label, creates a person who is now considered a deviant. For Becker, the change from
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citizen to outsider or deviant is the equivalent to a change in social status. The impact of labeling on the self-concept has received mixed support. Chambliss’ (1973) comparison of two middle- and lowerclass gangs indicated that members’ self-concepts reflected the way community members viewed the gang. Specifically, middle-class residents viewed members of their community gang as pranksters, who, in turn, viewed themselves as simply out to have fun. On the other hand, lower-class gang members, who were viewed as delinquent, incorporated this perception into their self-images by purposefully behaving offensively in public and seeking out other delinquents. In a study of 96 adjudicated and 105 nonadjudicated males, Hepburn (1977) found that, compared to nondelinquents, delinquent males had a lower self-concept, identified themselves as delinquent, intended to remain delinquent and seek similar others, and disdained police. However, when Hepburn controlled for socioeconomic status and self-reported delinquency between both groups, he found no relation between incarceration and low self-concept. This suggests that low self-concept might be related to economic hardship and self-reported problem behaviors. In another study, Gibbs (1974) found that 26 adolescent males arrested and processed repeatedly for car theft viewed themselves as more delinquent after the arrests than after court appearances where they were officially labeled delinquent. In fact, participants reported higher self-esteem after the court appearance. Foster, Dinitz, & Reckless (1972) also found that males, who had been arrested, processed, then interviewed three weeks later reported no changes in self-concept, and that 40% reported favorable outlooks on obtaining subsequent employment and completing school. Despite these unexpected findings, other research has found support for police contact and subsequent delinquency, especially among white males (Ageton & Elliott, 1974) across various levels of socioeconomic status (Jensen, 1980), and among light offenders (Lipsit, 1968; Snyder, 1971). Some researchers have suggested that labeling theory is more conducive to qualitative evaluations than to empirical evaluations (Gibbs, 1974; Shoemaker, 2000 ), and others argue that the theory does not account for reductions in delinquency with age (Nettler, 1984).
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Social Process Theories Social process theories focus on the interactions of the individual and the environment that result in delinquent behaviors. Social process theories include differential association (Sutherland, 1939), drift theory (Matza, 1964; Sykes & Matza, 1957), containment theory (Reckless, 1967), and social control theory (Hirschi, 1969). Differential association focuses on how adolescents learn about delinquency. Drift theory proposes neutralization techniques adolescents use to flow in and out of committing delinquent behaviors. Containment theory examines how internal and external pressures combine to contain delinquent behaviors. Social control theory addresses how an individual’s level of social integration diminishes or encourages delinquency. Developed in the 1920s, Sutherland’s (1939) theory of differential association sought to improve upon macro perspectives that failed to explain how macro-level forces transform into individual behaviors (Shoemaker, 2000). Sutherland based his theory on several assumptions: human behavior is flexible and situationally-based, delinquents are not deviant or bad since they also engage in socially accepted behavior, and most delinquent behavior occurs in a group or gang context. Instead of arguing that certain aspects of society are disorganized such that they lead its members to engage in delinquent acts, Sutherland (1939) suggests that certain aspects of society are “organized” to encourage delinquency. He believed that some form of social organization is inherent and present in any society. Consequently, engaging in delinquent acts is a result of having excess attitudes that favor norm violation acquired while associating with others either directly or distally (Sutherland & Cressey, 1978). Sutherland proposes two pathways to delinquency that operate at the individual and social levels. At the individual level, adolescents engage in delinquency as a result of delinquent associations. At the societal level, adolescents engage in delinquency because society is “differentially organized” such that it fosters exposure to, and influence by, delinquent values. The macro component of the differential social organization theory has not been heavily studied since it is much more difficult to analyze, and this theoretical component lacks conceptual clarity (Bartol & Bartol, 1989; Bartollas, 2003; Shoemaker, 2000). Jensen (1972)
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nevertheless, found support for components of differential association, including the attenuating role of parental monitoring on delinquency. In addition, Thompson, Mitchell, and Doddler (1984), and Matsueda and Heimer (1987), have found that differential association provides a better explanation of delinquency than social control theory. Similar to Sutherland’s assumption that delinquency is the product of excess conditions that favor it, Sykes and Matza’s (1957) drift theory posits that delinquency is the product of feelings of injustice and situational group justifications that encourage delinquency. According to the authors, adolescents are able to drift in and out of delinquency by neutralizing conditions that discourage delinquency. Sykes and Matza suggest that, in contrast to Cohen (1955) theory, adolescents need not be part of a sub-culture of delinquency that is completely at odds with conventional society. Indeed, they suggest that adolescents who engage in delinquency recognize and identify with conventional social norms. This is evidenced by adolescents’ a) feelings of remorse after committing a crime b) admiration or respect for societal icons of conventional or law-abiding behaviors (e.g., priests, sports figures), c) norms regarding who or what cannot be victimized or vandalized, and d) associations with family members who have conventional beliefs against delinquency. In order to engage in delinquent activity, adolescents will make justifications or rationalizations to protect themselves “from self-blame and the blame of others after the act (Sykes & Matza, 1957, p.666).” An adolescent becomes a delinquent (i.e., drifts into delinquency) when he/she learns to neutralize or deflect the guilt from violations of internalized social norms. According to Sykes and Matza, an adolescent drifting into delinquency may use any of five techniques of neutralization. Denial of Responsibility includes claims that the offending behavior was an accident or a result of some external influence (e.g., “I didn't mean it”). Denial of Injury refers to claims that no actual harm has been done (e.g., "I didn't really hurt anybody"); and is often used for property crimes where the property owner is seen as someone who is unharmed as he/she is clearly able to afford the property. Denial of the Victim refers to claims that the recipient of any injury was deserving of such harm (e.g., "he had it coming to him"). Condemnation of the Condemner reflects attempts by the adolescent to shift the focus from his/her behavior by impugning the intentions of conventional others who are in a position to judge his/her actions (e.g.,
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“everybody's picking on me” and “they’re no better than I am”). Appeal to Higher Loyalties are claims that any infractions were in response to nobler or more important causes (e.g., assaulting someone in order to defend one’s honor or "I didn't do it for myself") According to (Matza, 1964), adolescents are able to use neutralization techniques because similar techniques are built into our legal system (e.g., denial of responsibility, injury, or victim). Punishments for crime are not always applicable and under certain situations criminal behavior may be justified (e.g., homicide resulting from self-defense). The model is not purported to represent all delinquents; however, it may account for most “ordinary” delinquents, especially those who desist their behaviors upon entering adulthood (Matza, 1964). Thus, the appeal and strength of drift theory lies in its ability to (at least conceptually) account for delinquent and nondelinquent behavior, as well as age-associated decreases in delinquency. Unlike Reckless’ (1967) containment theory or Hirschi’s social control theory (1969), drift theory does not assume that adolescents need to be constrained from engaging in delinquency. Rather, drift theory posits that adolescents must engage in the exercise of neutralization in order to commit an offense. Similar to differential association, the peer group is important within the drift theory framework because peer influence is a form of neutralization. Drift theory has received some criticism (Giordano, 1976; Hindelang, 1970; Hindelang, 1974), specifically to claims that adolescents may not engage in neutralization if they are not invested in conventional values. More recent longitudinal (Agnew, 1994) and international (Agnew, 1994; Landsheer & Hart, 2000) studies have found support for neutralization techniques and evidence that offenders distinguish between applicability of punishments based on offenses. These findings support the premise that adolescents distinguish offenses and victims; a necessary condition for effective neutralization. Reckless’ (1967) containment theory claims that, left unconstrained, adolescents will engage in delinquency. Factors that instigate delinquent behaviors are considered internal and external “pushes.” Internal pushes include drives, motives, hostility, and feelings of inferiority (Bartollas, 2003). External, or environmental, pushes include poverty, social strain (i.e., lack of economic opportunity), conflict, and minority group status. Buffering individuals
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from these risk factors are internal and external controls. Internal controls include self-control and a positive self-concept. External controls involve, but are not limited to, parental supervision and alternate recreational activities. Reckless’ theory suggests that internal and external buffers complement each other such that, when one is weak or overwhelmed, the other steps in to attenuate the possibility of engaging problem behaviors. While external controls have not received much research attention, internal controls have received some support. For example, Jensen (1972) found high self-esteem and a positive self-concept were related to less delinquency. However, Wells and Rankin (1983) found no association between self-concept and delinquency, and Ageton and Elliott (1974) have found police contact to be detrimental to selfconcept only among white males. Reckless’ external control is reminiscent of Hirschi’s social control theory. Social control theory claims that adolescents who are bonded to their society will be less likely to commit delinquency. Hirschi’s social bond theory is comprised of four elements: attachment to conventional others, commitment to conventional activities, involvement, and beliefs. Attachment to conventional others reduces delinquency since adolescents are motivated not to engage in problem behaviors that may jeopardize their relationships. Similarly, adolescents who are committed to conventional activities are less likely to engage in delinquency since doing so forfeits their investment in those activities. In a related vein, involvement in prosocial activities reduces delinquency by consuming an adolescent’s time that might otherwise be used to offend. Last, a lack of prosocial beliefs can promote delinquency by diminishing respect for authority and for prosocial norms. Using a sample of over four thousand high school students in Seattle to examine his predictions, Hirschi (1969) found delinquency was associated with parental bonding but not peer bonding. Delinquency also was associated with poor commitment, involvement, and beliefs. Although studies by Thompson et al. (1984) and Matsueda and Heimer (1987) have found support for Sutherland’s differential association over social control theory, a multitude of research supports the inverse relationship between social control and delinquency in school settings (Cernkovich, & Giordano, 1992; Zingraff et al., 1994) and in general (Bartollas, 2003; Shoemaker, 2000).
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Shoemaker’s Integrated Theory of Delinquency In contrast to the limited scope of singular theories of delinquency discussed above, the work of Shoemaker (2000) attempts to summarize these individual theories of delinquency into a single integrative model. Shoemaker proposes an Integrative Model of Delinquency that incorporates general constructs that operate at three conceptual levels: Structural, individual, and social psychological (see Figure 2.5). The inclusion and location of these keyed constructs are guided by the theoretical models presented above and are discussed in further detail below. The strength of association between these constructs is denoted by the characteristics of the pathways in the model. Solid lines represent moderate to strong associations, dotted lines represent weaker associations, and double-pointed arrows represent reciprocal influences. Figure 2.5 Shoemaker's (2000) Integrated Theory of Delinquency in a Social Systems Theory Framework
The causal pathway of the model extends from left to right and results in delinquency. Structural conditions refer to socioeconomic
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variables and reflect the theoretical contributions of macro level theories to delinquency, including Merton’s (1937) strain theory and Shaw and McKay’s (1949) social disorganization theory. The individual characteristics construct refers to biological traits and “personality configurations” (p. 268), and is consistent with Reckless’ containment theory (1967). Thus, the influence of self-esteem also is represented by containment theory. Finally, peer influences reflect components of Sutherland's theory of differential association, and these peer influences are also present in Matza’s drift theory (that depends on the role of peer influence and modeling). After establishing the use of general constructs to be included in a delinquency model, as well as the direction of the associated pathways, this review proceeds to map Shoemaker’s (2000) integrated model on the ecological map. Specifically, the general constructs and pathways in Shoemaker’s model are super-imposed onto the adapted ecological model of behavior in Figure 2.6. In order to accurately map Shoemaker’s model, two conceptual adjustments were made; these changes are based on the same logic used to develop the adapted ecological framework. The first and most critical adjustment is the location of the delinquency construct within the behavior system. Defining delinquency as part of the behavior system is consistent with Jessor and Jessor’s (1977) PBT and it allows one to adequately reflect the impact of all infra-, micro-, meso-, exo-, and macrosystem variables on delinquency. The second change that warrants attention is the integration of self-esteem and the individual system. This decision is consistent with Bartol and Bartol’s (1989) recommendation that biological, physiological, and personality constructs should be subsumed under the infrasystem when such variables are employed in a systems theory framework. Moreover, this change also reflects the contribution of Jessor and Jessor’s (1977) personality system to systems theory. Summary of the Conceptual Model of Problem Behaviors The purpose of this project is to test an ecological model of delinquency. After adapting Bronfenbrenner’s social system’s framework, individual models of delinquency and problem behaviors were discussed in relation to that framework. An integrative model of
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delinquency by Shoemaker (2000) also was discussed in order to derive the generalized constructs that should be included in a model of problem behaviors. The result of the process is conceptual model of problem behaviors. The steps undertaken thus far correspond with the second row of Figure 1.1. Figure 2.6 Shoemaker's (2000) Integrative Model of Delinquency in an Adapted Systems Theory Framework
The final step in the development of an ecological model of delinquency is to select specific constructs that are congruent with the ‘generalized’ constructs that have been derived from the literature. Consistent with systems theory, the specific constructs also must fall on the proper ecological level in the adapted framework. Eight specific constructs derived from an existing dataset are discussed in relation to
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their conceptual operating levels (i.e., personal, interpersonal, and contextual). A description of these eight specific constructs is provided below, followed by a discussion of the hypothesized model of problem behaviors. Risk and Protective Factors Relating to the Hypothesized Model of Problem Behaviors Personal (Protective) Factor: Locus of Control Rotter (1966) defines locus of control as a generalized expectancy of internal or external control reinforcement, where reinforcement represents the outcomes of one’s behavior. An adolescent who is internally controlled believes that reinforcement (i.e., outcomes of one’s behavior) is due to his or her abilities or efforts. An externally controlled adolescent believes that reinforcement is due to fate, chance, or some powerful external force. Locus of control is not dichotomous but extends along a continuum and may vary situationally. With regard to the models discussed above, Jessor and Jessor (1977) view locus of control as part of the personality system that mediates the influence of socialization and social structure on human behavior. Jessor and Jessor posit that an internal locus of control will be negatively associated with delinquency, whereas an external locus of control will be positively associated with delinquency. Specifically, Jessor and Jessor (1977) argue that a “...belief in internal locus of control reflects a commitment to the ideology of the larger society, a relatively conventional perspective that, unlike an external orientation, functions to safeguard conventional behavior and to protect against nonconformity (p. 21).” Furthermore, adolescents with an external locus of control are expected to engage in more problem behaviors since the consequence of their bad behavior is not connected to personal decisions, but rather is dependent on external forces. In their study of 188 male and 244 female high school students, Jessor and Jessor (1977) found a negative association between internal locus of control and problem behaviors (drug use, deviant behavior, multiple problem behavior) among high school males. Subsequent studies support the positive relationship between external locus of control and delinquency, as well as the mediating role played by locus of control on the association between delinquency and
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family variables. For example, in a study of 157 undergraduates, Sadowski and Wenzel (1982) found that students with external locus of control scored higher on self-reported hostility and aggression. In a study of 43 white and 16 Polynesian delinquent and non-delinquent adolescent males, Parrott and Strongman (1984) found delinquency was associated with external locus of control and with family experiences. Shaw and Scott (1991) also investigated the influence of parental discipline style on delinquent behavior among 201 adolescents and 30 male adjudicated delinquents. These authors found that inductive parenting style, defined as more open and communicative, was negatively related to delinquency, and that this association was mediated by internal locus of control. In addition, punitive parenting style17 and love-withdrawal18 parenting style were positively related to delinquency, which were mediated by external locus of control. More recently, a study by Peiser and Heaven (1996) examined links between perceived family relationships, parental discipline style, locus of control, self-esteem, and self-reported delinquency among 177 Australian teenagers (aged 15-16 years). Results showed that low selfesteem (r = .31, p < .01) and external locus of control (r = .31, p < .01) were related to high self-reported delinquency. Moreover, inductive parenting style was predictive of low female delinquency while punitive parenting style was predictive of high male delinquency. Importantly, however, self-esteem and locus of control did not mediate any of these effects on delinquency. The authors suggest that Shaw and Scott’s (1991) initial findings may be the result of a larger sample size and the use of less sophisticated regression analyses. Additional studies also reveal a link between locus of control and problem behaviors. For example, in a sample of 241 adolescents, Downs and Rose (1991) categorized participants into four groups based on their risk profiles. The most at-risk group of adolescents had little school involvement, was labeled negatively by peers, had positive attitudes towards drug use, low self-esteem, diminished access to occupational opportunities and societal estrangement, and, most importantly, had an external locus of control. Finally, Hirata, 17
Also characterized as authoritarian, it is defined be using more forceful disciplinary measures. 18 Defined as parental affection that is dependent child’s compliance to parental demands.
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Watanabe, and Souma, (1998) examined 194 delinquent and 314 nondelinquent junior high school boys’ perceptions of school environment and locus of control. Hirata et al. found that delinquent boys had a more external locus of control than the non-delinquent boys, demonstrated more negative cognitions toward their teachers, stronger isolation in class, and a diminished sense of rules. Overall, the literature on locus of control indicates a positive association between external locus of control and problem behaviors. Personal (Protective) Factor: Anger Control Anger control will be operationalized as an adolescent’s disposition to manage his/her temper in different situations. In a recent study of 13 to 18 year-old adolescents, Coles, Greene, and Braithwaite (2002) report that delinquent males were more likely to score higher on measures of anger and anxiety than non-delinquent males. In a study of 452 African-American sixth graders in New York City, Griffin et al., (1999) found that anger control was negatively related to interpersonal aggression, a form of problem behaviors. Furthermore, anger control mediated the relationship between parental monitoring and aggression, though it was not related to peer delinquency or neighborhood risk. Additional support for a relationship between anger control and delinquency comes from studies examining impulsivity and delinquent behaviors. Gottfredson and Hirschi (1990) propose that the shortedsightedness displayed in the inability to control one’s impulses is tantamount to the shortsightedness employed in criminal behavior (i.e., instant gratification, little or no regard for others). Hirschi and Gottfredson (1993) and others (e.g., Gibbs, Giever, & Martin, 1998) define self-control not as a trait, but more so a “broad disposition that has to do with individual assessments of the consequences of actions and interpretations of situations” (p. 43). In a study of 289 college students, Gibbs et al. (1998) explored whether self-control mediated the relationship between parental management (i.e., parental monitoring and discipline) and delinquency (i.e., cutting class, cheating in ninth grade and first year of college, alcohol consumption). They reasoned that parental monitoring, followed by identification of norm violation and appropriate disciplining, would foster an individual's ability to recognize deviant behavior, consider its consequences, and avoid
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engaging in that behavior. Results indicated that self-control mediated the relationship between parental management and delinquency, supporting Gibbs et al.’s contention. Finally, a study by Pfefferbaum and Wood (1994) examined the relationship between thrill-seeking and delinquency. These authors found a gender-by-self-control interaction such that males who measured high in thrill-seeking behavior and low in self-control reported significantly more property delinquency. Interpersonal (Risk) Factor: Negative Peer Influence Negative peer influence, or peer pressure, refers to the actual or perceived persuasion of an individual by his/her friends or acquaintances. Peer influence has been associated with delinquency (Pleydon & Schner, 2001), adolescent drug use (Flannery, Williams, & Vazsonyi, 1999), alcohol use, cigarette smoking, and general problem behaviors (Farrell, Kung, White, & Valois, 2000). Specifically, Farrell et al. found that peer pressure for drug use was associated with a higher order construct of ‘general problem behavior’ (i.e., drug use, delinquency, and aggression; r = .55). In addition, peer pressure was directly related to drug use (r=.25) among rural and urban subsamples, as well as across genders. In addition, Flannery et al. found greater susceptibility to peer pressure, and higher levels of aggression, delinquency, and drug use among unsupervised adolescents. Similarly, peer pressure was associated with delinquency among a sample of Caucasian and Mexican-American 12 year-olds (Fridrich & Flannery, 1995), though this relationship was mediated by parental monitoring, regardless of ethnicity. Specifically, peer pressure was negatively associated with parental monitoring, which, in turn, was negatively related to delinquency. Most recently, Pleydon and Schner (2001) found higher levels of peer pressure (compared to controls) among a small sample of delinquent girls. Overall, peer influence seems to be strongly associated with problem behaviors, at times mitigating the effects of parental monitoring on delinquency and drug use. Interpersonal (Protective) Factor: Parental Monitoring. Several studies have demonstrated a positive influence of parental monitoring on aggression and problem behaviors. For example, Morrison, Robertson, Laurie, and Kelly (2002) found parental monitoring and perceived social support predicted reductions in
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antisocial behavior among Latino adolescents. Griffin et al. (1999) examined the impact of parental monitoring on aggression in a sample of African-American sixth graders. Results showed that parental monitoring was associated with less aggression and that the relationship was mediated by anger control skills. Thus, parental monitoring was indirectly and negatively related to aggression through its direct link with anger control skills. In a subsequent study of sixth graders, Griffin, Botvin, Scheier, Diaz, and Miller (2000) found that parental monitoring was negatively associated with delinquency and drinking behaviors among boys, and that unsupervised time at home was associated with smoking for girls. Finally, Jacobson and Crockett (2000) reported a negative association between parental monitoring and delinquency. Importantly, however, this relationship was simultaneously moderated by gender and grade. That is, results showed that the positive effect of parental monitoring on delinquency was most profound for boys at higher grade levels whereas it was most profound for girls at lower grade levels. In sum, parental monitoring appears to be strongly associated with delinquency and aggression, two components of generalized problem behavior. Interpersonal (Risk) Factor: Family Conflict A growing number of studies have demonstrated the deleterious effect of conflict within the family on adolescents’ externalizing behaviors. For example, Gorman-Smith, Tolan, Loeber, and Henry (1998) found that families of chronic adolescent offenders are characterized by conflict, lack of parental involvement, and neglect. In addition to the actual circumstances within a family influencing adolescent behavior, perceptions of family functioning seem to play an important role. Shek (2002) found that positive perceptions of family functioning were associated with fewer problem behaviors, including delinquency and drug use, in a sample of at-risk Chinese adolescent females. In a study examining verbal conflict resolution among serious violent delinquent offenders and their families, Williams and Borduin (1997) found that gender moderated the relationship between conflict resolution and delinquency. That is, compared to families with delinquent females, families with delinquent males had more problems, and these families were less likely to solve their problems through verbal conflict resolution strategies.
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Similar results have been found among African-American and New Zealand samples. In a study of at-risk African-American adolescents living in a high-crime area, DuRant, Cadenhead, Pendergrast, Slavens, and Slavens (1994) found that reported use of violence was associated with family conflict, victimization, and corporal punishment. Finally, Fergusson, Horwood, and Lynskey (1992) recorded the developmental effects of family conflict on delinquency in a sample of New Zealand children. Specifically, results showed that exposure to family conflict during childhood was associated with delinquency at age 13. Interpersonal (Protective) Factor: Social Bond Social bond describes the extent to which an adolescent: a) feels an attachment to parents, teachers, or peers, b) feels committed to engaging in prosocial activities, c) is involved in prosocial activities, and d) holds beliefs reflecting normative values. Hirschi (1964) tested his control theory on a sample of 1,300 middle school and high school students. He assessed the four components of his social bond construct and found that attachment to parents, teachers, or peers was the factor most strongly associated with reductions in delinquency. Specifically, Hirschi found that boys who communicated intimately with their fathers were less likely to engage in delinquent behaviors. In the present study, social bond was measured with a scale assessing perceived social support (which is discussed further in Chapter 3). While perceived social support does not capture all four components of Hirschi’s construct of social bond, this measure is theoretically consistent with the attachment component described above. Hirschi (1964) also has found that the attachment component was the strongest predictor of reductions in delinquent behaviors. The perceived social support scale used in this study measures the extent to which respondents believe that adults, peers, teachers, and family members care about him or her, and therefore reflects attachment similar that referred to by Hirschi. Furthermore, recent research has demonstrated negative associations between perceived social support and delinquency. For example, Morrison, Robertson, Laurie, and Kelly (2002) found that perceived social support predicted reductions in antisocial, delinquent behaviors among Latino adolescents.
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Contextual (Risk) Factor: Neighborhood Risk In addition to the above-mentioned correlates of delinquency, studies have shown that perceived neighborhood risk for crime/criminal behavior is related to adolescent delinquent behavior. In a secondary analysis of 1986 data on 1,775 urban middle- and high school students, Williams, Singh, & Singh (1994) found that over 57.5% of adolescents who were concerned about crime in their neighborhood had decided to use a night-time escort, 18.9% learned self-defense, and 10.2% carried mace. Those who witnessed a crime in their neighborhood were more likely to learn self-defense and carry mace and less likely to use a night escort. These results suggest that perceived neighborhood risk leads to behavioral changes that may place adolescents are greater risk for victimization. Griffin et al., (1999) found that perceptions of neighborhood risk were related to interpersonal forms of aggression among youth. Furthermore, this relationship was partially mediated by adolescent risk taking. In other words, perceptions of neighborhood risk were positively associated with risk taking behaviors, which, in turn, were positively related to interpersonal aggression. Additionally, in a study of adolescent externalizing behaviors, Pettit, Bates, Dodge, and Meece (1999) found neighborhood risk mediated the relationship between lack of supervision and adolescent problem behaviors. Specifically, lack of supervision by parents and other adults was indirectly related to externalizing problems via its direct association with perceptions of neighborhood risk. Moderators Gender Differences in recorded and self-reported delinquency denote a higher rate of delinquency among male adolescents (Bartol & Bartol, 1989; CDC, 2002; FBI, 2002a). Girls equal or exceed boys in status offenses, while boys surpass girls in violent offenses (Chesney-Lind & Paramore, 2001; Gottfredson, Sealock, & Koper, 1996). This pattern is also evident in Table 2.1 where arrests trends from 1993-2001 show increases for females in all categories except, homicide, robbery, forcible rape, burglary, and motor-vehicle theft. Although males have greater nominal rates of offending, recent trends show recent increases
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in aggravated assault, larceny, arson, vandalism, weapons-carrying, and other assaults, (see Tables 2.1). Females also demonstrate discernable differences in status and sex-related offenses including: prostitution, curfew, loitering, disorderly conduct, and running away. These statistical differences in arrest rates (e.g., FBI, 2002) however, are partially a result of selection/enforcement biases among police officers and court officials. These officials view girls as more vulnerable than boys and disproportionately arrest and charge girls for minor offenses such as running away (where females nominally exceed males) (Chesney-Lind, 2002; Chesney-Lind, 1988; Shelden & Chesney-Lind, 1993). National data on self-reported weapon-use in school among females (Table 2.2), shows a 6.9% increase between 1993 and 2001, while males reported a 1.9% decline. Nevertheless, in study of adolescent crime in Honolulu, (Chesney-Lind & Paramore, 2001) found that suggested increases in female aggression between 1991 and 1997 was a result of increased reporting of less serious crimes, involved less lethal weapons (i.e., knifes v. firearms), among mostly female victims. To the extent that the Honolulu study reflects national patterns, it is not clear whether increases in female offenses in the same period reflect true changes or biased enforcement. Self-reported instances of forced sexual intercourse declined between 1999 and 2001 among males and females. Females however, accounted for 68% of self-reported sexual assaults in 2001. This finding is consistent with literature showing that female delinquents are more likely to be the victim of sexual assault (Chesney-Lind, 1988; Chesney-Lind, 1989; Chesney-Lind, 2001). The above trends showing differential treatment by law enforcement regarding sex-related and status offenses, as well as high rates of sexual assaults also are consistent with calls (Klein, 1973) for female theories of delinquency “that are sensitive to patriarchal as well as class and racial context (Chesney-Lind’s,1989, p.26).” Kline and Chesney-Lind argue that a majority of delinquency theories do not adequately explain the etiology of female delinquency because they fail to include factors (e.g., gender oppression, paternalism) that explain both the cause of initial female delinquency and differences in the types of offenses. They argue that paternalistic and sexist views that value women based on their sexual purity consequently leads to continuous social pressure that actually serves to foster and promote female
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problem behaviors. For example, at the turn of the century, exaggerated concerns over female promiscuity led to institutionalization of females under harsher conditions and longer periods than their male counter parts (Chesney-Lind, 1989). More recently, efforts to reduce female problem behaviors are reflected by disproportionate arrests and prosecution for status offenses, such as running away. Chesney-Lind argues that female delinquents are more likely to be sexually abused. Often, adolescent females experience sexual abuse at home by a close relative or acquaintance. Many girls escape this situation by running away. After running away, girls are further victimized when they are arrested and forced to return to abusive home settings. Females who remain on the street are likely to abuse drugs and engage in prostitution to support a drug habit or simple sustenance (Chesney-Lind, 1989). Prostitution becomes a viable enterprise for female runaways when sex is the only commodity that is valued in a paternalist culture. Involvement in prostitution and drugs increases the likelihood that a female will be exposed to the justice system, where they are further victimized and once again, overrepresented in sex-related arrests (Chesney-Lind, 1988). To Kline (1973), theories and statistics that suggest that status- and sex-related offenses are female crimes is tautological. Paternalistic values regarding how females should behave define female delinquency. Societal pressures/stressors (e.g., sexual abuse, gender role oppression) stemming from these paternalistic values serve to promote minor female offenses (e.g., running away) which later leads to more serious or sex-related offenses (e.g., theft, prostitution). Chesney-Lind recommends that a female approach to delinquency a) have an explanatory structure that is sensitive to a patriarchal context, b) is sensitive to processes or situations that perpetuate problem behaviors among females, and c) explores “ways in which agencies of social control---the police, the courts, and the prisons---act to reinforce a woman’s place in society (p.19).” Overall, these recommendations suggest that a model of female delinquency must account for the unique factors that contribute to female problem behavior and societies role in perpetuating female problem behavior. The current hypothesized model does not include a macro level construct, although the adapted framework can accommodate distal yet potentially powerful predictors of female problem behaviors (e.g., paternalistic culture). The study of female delinquency under problem
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behavior also may be a fruitful avenue of research for several reasons. First, the problem behavior construct is less value-laden than delinquency. That is, the generalized notion of problem behavior is viewed as a normal aspect of adolescence (Jessor et al., 1995). Furthermore, according to Chesney-Lind (1989), female adolescent problem behavior, such as running away, can be viewed as normative, when stressors (e.g., sexual abuse) that lead girls to run away are considered. Delinquency thus can be viewed as value-laden in the sense that it is comprised of behaviors that elicit societal/legal efforts to control them. To the extent that such control efforts are biased towards a segment of the population and have their basis on gender roles for women (e.g., persons requiring extra protection by society), it can be said that delinquency is value-laden. This argument is consistent with Klein’s and Chesney-Lind position that female delinquency was not so much a movement to protect adolescents or society from harm but to control female behavior (Klein, 1973; Chesney-Lind, 1989; Platt, 1969). Second, this outcome variable for the current study includes status offenses (e.g. running away) which according to Chesney-Lind (1989) are “trivial offenses [that] dominate both male and female delinquency” yet “are more significant in the case of girls (p.7)” who are over represented in status related arrests. Although feminist theory suggests that macro-level factors may be the most relevant explanatory factors of problem behaviors among females, other studies also provide some indication regarding gender differences for factors related to problem behaviors. For example, Griffin et al. (1999) report that compared to girls, boys were more likely to “fight if provoked (p.288).” Research also indicates that females are more likely to engage in verbal forms of aggression compared to males (Loeber & Stouthamer- Loeber, 1998; McGee, Feehan, Williams, & Anderson, 1992). In addition, studies exclusive to male adolescents support an inverse relationship between internal locus of control and delinquency (Hirata, Watanabe, & Souma, 1998; Parrott & Strongman, 1984; Peiser & Heaven, 1996), and others have found significant relationships among high school boys but not girls (Jessor & Jessor, 1977). In a study of 1,895 middle school students Farrell et al. (2000) report that boys had higher prevalence rates than girls for drug use, non-violent delinquency, aggression, and non-physical aggression (e.g., insults). Overall, these findings suggest that relationships among risk and protective factors and delinquency may be moderated by
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gender. The hypothesized model will be tested among male and female subgroups to ascertain whether the models are representative of male or female problem behaviors. Rural/Urban Location Studies examining the characteristics of urban locations and crime date to the middle of the last century (Park and Burgess, 1924; Shaw & McKay, 1950). However, studies comparing rural and urban differences in delinquency are rare (Gardner & Shoemaker, 1989; Osgood & Chambers, 2003 (Farrell et al., 2000). Existing evidence points to greater prevalence of problem behaviors and victimization in urban areas (Gottfredson et al., 1996). In a cross-sectional study of 277 urban and 456 rural adolescents, Gardner and Shoemaker (1989) found lack of social bonding and attachment to delinquent peers were related to increased delinquency among urban adolescents. Although, attachment to conventional peers was related to less delinquency among both groups. Current studies also have found associations between social bond and adolescent problem behaviors. Duncan, Duncan, and Stryker (2002) found low SES neighborhoods were associated with and sales of alcohol and lower neighborhood cohesion. In turn, lower neighborhood cohesion was associated with greater perceived neighborhood problems regarding adolescent drug and alcohol use, and actual adolescent drug and alcohol arrests. A more recent analysis (Osgood & Chambers, 2003) of crime data from 264 rural counties in Florida, Georgia, Nebraska, and South Carolina indicated that residential instability, ethnic diversity, and family disruption were related to delinquency. These findings are consistent with social disorganization theory which holds that delinquency should rise in communities with high mobility since such mobility reduces the opportunity to form strong bonds with others. Ethnic diversity should lead to delinquency in rural areas if cultural differences lead to distrust and if low population densities do not provide enough similar others with whom to form a bond. In addition, family conflict or disruption is consistent with social disorganization theory as the lack of adequate communication should lead to less supervision of adolescents. The impact of family conflict on delinquency would be larger in rural areas where support networks might be smaller.
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Farrell et al. (2000) found that male and female middle schools in urban areas were more likely to use drugs, engage in non-violent delinquency, physical aggression, and non-physical aggression than their rural counterparts. These results however, were confounded by age; students in the rural sample were sixth graders while students in the urban sample were seventh graders. The additional year may have provided urban students additional opportunities to engage in problem behaviors. Overall, the limited research on rural/urban differences and suggest a higher prevalence of problem behaviors in urban areas. Given these findings, the hypothesized model will be tested among rural and urban subgroups19 to ascertain whether the models are representative of rural or urban problem behavior. Hypothesized Model of Problem Behaviors Developed from the literature summarized above, the hypothesized model for this project is illustrated in Figure 2.7. The hypothesized model contains two personal level constructs (anger control and internal locus of control) that form the infrasystem component of the model. Family conflict, parental monitoring, negative peer influence, and social bond involve the interaction between an adolescent and his /her family, parents, peers, and close others, respectively, and constitute the situational (interpersonal and perceived) portion of the model. From a social systems perspective, each of the four constructs represents a microsystem. Furthermore, the interaction between any two microsystems (e.g., family conflict Î parental monitoring, social bond Î negative peer influence, and parental monitoring Î negative peer influence) denotes a mesosystem. A mesosystem also is topographically implied by a construct that is located between the microsystem area and the mesosystem area of Figure 2.7. Neighborhood risk forms the contextual portion of the model. Given that neighborhood risk is conceptualized as a distal construct whose influence is mediated by
19
Rural and urban classifications were determined from Census status. School sites located in a community with less than 50,000 residents were considered rural; while communities exceeding that limit were considered urban.
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microsystem construct, one can classify that relationship as an exosystem.20 Figure 2.7 Hypothesized Ecological Model of Problem Behaviors
The fit of the hypothesized model of problem behaviors represents the research question for this project: Is the etiology of problem behaviors adequately described by an ecological model comprised of 20
According to Bronfenbrenner (1979) an exosystem allows for a construct to have a partially direct impact on the individual. However, its effect must be at least partially mediated by a microsystem construct. Given that neighborhood risk is measured via respondent observations, one can assume that there is at least some direct effect of Neighborhood Risk on the individual. However, given the proposed theoretical model, it is believed that the effect of Neighborhood Risk on delinquency is mediated.
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personal, interpersonal, and contextual factors? Integrated within this research question are theorized direct relationships and mediated pathways that provide the underlying theoretical mechanisms through which problem behaviors/delinquency are fostered or averted. While the adequacy of the overall conceptual model is the focus of this study, seven hypothesized mediated pathways underscore the underlying premise of the model. Specifically, the model suggests that influences (i.e., factors) within more distal systems (i.e., ecological levels) are mediated by influences in systems that are more proximal. The seven hypothesized mediated pathways are discussed below. The direct and mediated relationships are illustrated in Figure 2.8 using “Path A” through “Path L” and “Path 1” through “Path 8.”
Figure 2.8 Conceptual Model with Prerequisite Pathways for Establishing Mediated Relationships
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Direct Effects Path A: (NEIGHBORHOOD RISK to FAMILY CONFLICT) Neighborhood risk is positively related to family conflict; adolescents who witness more community crimes will have more violent and conflicted family interactions. Path B: (FAMILY CONFLICT to ANGER CONTROL) Family conflict is negatively related to anger control; adolescents who have violent family interactions will exhibit less anger control. Path C: (ANGER CONTROL to PROBLEM BEHAVIORS) Anger control is negatively related to problem behaviors; adolescents who can control their anger will commit fewer problem behaviors.
Path D: (FAMILY CONFLICT to PARENTAL MONITORING) Family conflict is negatively related to parental monitoring; adolescents who have violent family interactions will perceive less parental monitoring. Path E: (PARENTAL MONITORING to PROBLEM BEHAVIORS) Parental monitoring is negatively related to problem behaviors; adolescents who believe their parents monitor them will engage in fewer problem behaviors. Path F: (NEIGHBORHOOD RISK to NEGATIVE PEER INFLUENCE) Neighborhood risk is positively related to negative peer influence; adolescents who witness more community crimes will experience more negative peer pressure.
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Path G: (PARENTAL MONITORING to NEGATIVE PEER INFLUENCE) Parental monitoring is negatively related to negative peer influence; adolescents who believe their parents monitor them will be less likely to succumb to negative peer influence. Path H: (NEGATIVE PEER INFLUENCE to INTERNAL LOC) Negative peer influence is negatively related to internal locus of control; adolescents who conform to peer pressure will see little connection between their own actions and the consequences of those actions. Path I: (LOC to PROBLEM BEHAVIORS) Internal locus of control is negatively related to problem behaviors; adolescents who have an internalized locus of control will engage in fewer problem behaviors. Path J: (NEIGHBORHOOD RISK to SOCIAL BOND) Neighborhood risk is negatively related to social bond; adolescents who witness more neighborhood crimes will experience a diminished social bond. Path K: (SOCIAL BOND to NEGATIVE PEER INFLUENCE) Social bond is negatively related to negative peer influence; adolescents who experience a strong social bond will conform less to peer pressure. Path L: (SOCIAL BOND to PROBLEM BEHAVIORS) Social bond is negatively related to problem behaviors; adolescents who feel a strong social bond will engage in fewer problem behaviors. Path M: (NEGATIVE PEER INFLUENCE to PROBLEM BEHAVIORS) Negative peer influence is positively related to problem behaviors; adolescents who conform to peer pressure will engage in greater problem behaviors.
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Indirect Effects Paths ‘A’ ‘B’ ‘C’: (NEIGHBORHOOD RISK to FAMILY CONFLICT to ANGER CONTROL to PROBLEM BEHAVIORS) Neighborhood risk is positively related to family conflict which, in turn, is negatively associated with anger control. Anger control, in turn, is negatively associated with problem behaviors. Adolescents who witness many community crimes will engage in more violent family interaction which will result in less anger control, leading to increased problem behaviors. This mediated path requires two a priori significant relationships – Path 1 and Path 2 in Figure 2.9a, shown below. Paths ‘A’ ‘D’ ‘E’: (NEIGHBORHOOD RISK to FAMILY CONFLICT to PARENTAL MONITORING to PROBLEM BEHAVIORS) Neighborhood risk is positively related to family conflict which, in turn, is negatively associated with parental monitoring. Parental monitoring, in turn, is negatively related to problem behaviors. Adolescents who witness many community crimes will engage in more violent family interactions which will result in less parental monitoring, leading to increased problem behaviors. This mediated pathway requires two a priori significant relationships – Path 2 and Path 3 in Figure 2.9b. Figure 2.9a Direct and Mediated Pathways Between Neighborhood Risk, Family Conflict, Anger Control, and Problem Behaviors
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Figure 2.9b Direct and Mediated Pathways Between Neighborhood Risk, Family Conflict, Parental Monitoring, and Problem Behaviors
Paths ‘F’ ‘M’: (NEIGHBORHOOD RISK to NEGATIVE PEER INFLUENCE to PROBLEM BEHAVIORS) Neighborhood risk is positively related to negative peer influence, which, in turn, is directly related to increased problem behaviors. Adolescents who witness many community crimes will conform to antisocial peer demands and engage in more problem behaviors. This mediated pathway requires one a priori significant relationship – Path 4 in Figure 2.9c.
Figure 2.9c Direct and Mediated Pathways Between Neighborhood Risk, Negative Peer Influence, and Problem Behaviors
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Paths ‘F’ ‘H’ ‘I’: (NEIGHBORHOOD RISK to NEGATIVE PEER INFLUENCE to INTERNAL LOCUS OF CONTROL to PROBLEM BEHAVIORS) Neighborhood risk is positively related to negative peer influence, which, in turn, is related to an external locus of control. Locus of control, in turn, is related to increased problem behaviors. Adolescents who witness many community crimes conform to antisocial peer demands, leading them to perceive their locus of control as external, resulting in greater problem behaviors. This partially mediated pathway requires two a priori significant relationships – Path 5 and Path M in Figure 2.9d. The relationship between negative peer influence and problem behaviors is predicted to be partially mediated such that the impact of peer influence on the decision to engage in problem behaviors will be direct (Path M) and indirect via Path I. This hypothesis is consistent with the literature showing a strong relevance of the peer group on adolescent behavior (Farrell, Kung, White, & Valois, 2000; Flannery, Williams, & Vazsonyi, 1999; Pleydon & Schner, 2001; Shoemaker, 2000). Figure 2.9d Direct and Mediated Pathways Between Neighborhood Risk, Negative Peer Influence, Internal Locus of Control, and Problem Behaviors
Paths ‘J’ ‘L’: (NEIGHBORHOOD RISK to SOCIAL BOND to PROBLEM BEHAVIORS) Neighborhood risk is negatively associated with social bond which, in turn, is negatively associated with problem behaviors. Adolescents who witness more community crime will experience a low social bond
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resulting in greater problem behaviors. This mediated pathway requires one a priori significant relationship – Path 4 in Figure 2.9e. Figure 2.9e Direct and Mediated Pathways Between Neighborhood Risk, Social Bond, and Problem Behaviors
Paths ‘G’ ‘H’ ‘I’: (PARENTAL MONITORING to NEGATIVE PEER INFLUENCE to INTERNAL LOCUS OF CONTROL to PROBLEM BEHAVIORS) Parental monitoring is negatively related to negative peer influence which, in turn, is negatively related to internal locus of control. Locus of control, in turn, is negatively related to problem behaviors. Adolescents who believe they are monitored conform less to negative peer influence, allowing them to view their locus of control as internal, resulting in fewer problem behaviors. This mediated pathway requires two a priori significant relationships – Path 6 and Path M in Figure 2.9f. The hypothesized partial mediation of negative peer influence here is consistent with Path ‘F’ ‘H’ ‘I’, above. Paths ‘K’ ‘H’ ‘I’: ( SOCIAL BOND to NEGATIVE PEER INFLUENCE to INTERNAL LOCUS OF CONTROL to PROBLEM BEHAVIORS) Social bond is negatively related to negative peer influence which, in turn, is negatively related to internal locus of control. Locus of control, in turn, is negatively related to problem behaviors. Adolescents with a
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strong social bond will conform less to negative peer influence, allowing them to view their locus of control as internal, resulting in fewer problem behaviors. This mediated pathway requires two a priori significant relationships – Path 7 and Path M in Figure 2.9g. The hypothesized partial mediation of negative peer influence here is also consistent with Paths ‘F’ ‘H’ ‘I’ and Paths ‘G’ ‘H’ ‘I’, above.
Figure 2.9f Direct and Mediated Pathways Between Parental Monitoring, Negative Peer Influence, Internal Locus of Control, and Problem Behaviors
Earlier sections reviewed several explanatory theories of delinquency across a variety of perspectives including individual, interpersonal, and structural. The following discussion addresses how these theories could be used to explain the pathways presented above. The purpose again is to provide a description of the underlying mechanisms that lend support to the overall hypothesized model. Not all of these theories, however, could be used to explain the current hypothesized model. For example, many of the biological and physiological theories are excluded as no physiological or biological data are available from this sample. By the same token, not all possible pathways suggested by the theories reviewed above are included in this model. As noted earlier (Shoemaker, 2000), proper theory building requires the inclusion of constructs based on theory and ‘logic.’ Not all of the operationalized
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constructs used to test this model lend themselves to the proper interpretation required to apply every theory. For example, Shaw and McKay’s (1949) social disorganization theory suggests that a structural level variable can have an impact on a family level variable (e.g., economic strain can limit the amount of time and resources parents have to monitor a child). Such an interpretation however, was not possible with our measures of neighborhood risk and parental monitoring. That is, none of the theories could logically explain how witnessing crimes was related to lower perceived parental monitoring.
Figure 2.9g Direct and Mediated Pathways Between Social Bond, Negative Peer Influence, Internal Locus of Control, and Problem Behaviors
Witnessing violence also could be related to conflicted and violent family interactions (Path A) as adolescents learn and model the behavior they witness in the home. This process is consistent with Sutherland’s (1939) theory of differential association whereby adolescents engage in antisocial (and prosocial) behaviors that they have come to learn (e.g., witness) when definitions are weighed in favor of that behavior (e.g., family discussion). Thus, the following discussion includes relevant theories that are (logically) applicable given the measures used in this model (see Chapter 3).
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The role of anger control and internal locus of control are not supported by any individual theory of delinquency. Instead, their location in the infrasystem derives from Bartol and Bartol’s (1989) argument for all physiological and personality constructs (that originate from within the individual) to be reconceptualized as an individual system. The inclusion of anger control is consistent with much literature, including links between aggression, impulsivity, and delinquency asserted by Eysenck (1977). Similarly, the inclusion of locus of control is based on noted findings and Jessor and Jessor’s (1977) contention that problem (or prosocial) behaviors are tied to particular personality orientations that encourage or discourage participation in said behaviors. The use of anger control and locus of control here is not to express a personality type per se, but to emphasize that a disposition to react or view the world in particular ways may be related to problem behavior, either directly (Path C and Path I) or as a mediator (Path B and Path H). The role of peers in this model can be explained by Sykes and Matza’s (1957) drift theory and Sutherland’s (1939) theory of differential association. As adolescents witness more community crimes, peers become an increasingly important microsystem. Peers may become the source of protection from crime victimization. Therefore, conforming to peer influence is an important consideration. Furthermore, differential association would suggest that as adolescents witness more crime (Path F), they become desensitized to it or choose to model the behavior. Through desensitization, adolescents may be left more susceptible to “definitions” (e.g., negative peer pressure) that encourage (initial or further) participation in those behaviors. The role of social bond and (to some extent) parental monitoring are best represented by Hirschi’s (1969) social control theory. Attachment to conventional others (i.e., feeling they care about you and spend time with you) reduces delinquency as adolescents are motivated not to engage in problem behaviors that may jeopardize their relationships. In the case of parental monitoring, the perception of being monitored reduces delinquency since getting caught also jeopardizes relationships. Nevertheless, the effectiveness of parental monitoring may reflect the consideration of some other punitive loss and not necessarily the loss of a communal social bond. In general, the mediated relationships between these constructs can be explained by differential association and by Reckless’ (1967)
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containment theory. From a differential association perspective, when the influence of a construct on delinquency is mediated by another construct, the valance of the mediated relationship will determine ‘definitions’ in favor of committing that behavior. For example, a protective factor such as anger control mediates the relationship between family conflict and delinquency (Path B and Path C) such that it reduces the definitions in favor of committing delinquency. According to Reckless (1967), factors that instigate delinquent behaviors are considered to be internal and external “pushes,” whereas factors that buffer individuals from these risk factors are internal and external “controls.” From a containment theory perspective, anger control (Path C) and locus of control (Path I) represent internal controls for delinquency. Parental monitoring (Path E and Path G) and social bond (Path L) represent external controls, whereas neighborhood risk (Path A, Path F, and Path J), family conflict (Path B and Path D), and negative peer influence (Path H) represent external pushes. No internal “pushes” are included in this model. The current model is based on a framework that can accommodate macro-level constructs (within the macro-system) and biological constructs (within an infra-system). Given the available measures in the existing data set however, the scope of the hypothesized model in this study is limited to personal, interpersonal, and contextual factors that reside in the infra-system, micro/meso-systems, and the exosystem, respectively. Although the current model does not test the adequacy of the entire adapted framework, the model contains several constructs spanning across sufficient ecological levels (i.e., systems) to address the research question. Overall, the hypothesized model of delinquency is well represented by social process theories of delinquency (e.g., differential association, drift, containment, social control). This should be expected since many of the microsystems are social-psychological constructs that reflect an adolescent’s perception of parents, peers, and community members (e.g., family conflict, parental monitoring, negative peer influence, social bond). The hypothesized model also incorporates mesosystem analyses involving sets of relationships between two microsystems: family conflict Î parental monitoring, social bond Î negative peer influence, and parental monitoring Î negative peer influence. Exploring mesosystem processes is important as it allows one to examine the etiology of delinquency in a more natural context wherein
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adolescents’ social groups interact with each other. Focusing studies primarily at the mesosystem level is consistent with Bronfenbrenner’s call for such research given that “the overwhelming bulk of research ….is limited to the microsystem level” (Bronfenbrenner, 1979, p. 224). As researchers answer Bronfenbrenner’s call and theory integration efforts (Shoemaker, 2000) become more ambitious, it seems Merton’s (1949) claim that we are not ready for grand theories may no longer hold true. However, as we focus on these larger theories, it may be wise to heed Merton and not forget the value of “better grounded theories of the middle range (p. 7).” The conceptual framework for this project originated from a large canvas (Figure 1.1) integrating systems theory, problem behavior theory, and resiliency theory. Through theory and logic, it has evolved into a manageable ecological model of problem behaviors exploring the relationships between personal, interpersonal, and contextual influences of delinquency, aggression, and drug use.
CHAPTER 3
Testing the model: A multi-state U.S. sample
SURVEY BACKGROUND Data for this study derive from in-school surveys of eighth grade students conducted during the 1998-2001 school years in selected Arizona, California, Nevada, and Wyoming schools. These data are part of a larger, multi-state, longitudinal project focused on resilience to youth violence.21 Students responded to a 142-item survey designed to assess participation in, and perceptions of, victimization as well as the perpetration of violence. The survey also assessed personal, peer, family, school, and community-related concerns and resources regarding the presence of violence in respondents’ everyday lives. Surveys were completed during school hours with parent permission. To ensure accurate and honest responses, the surveys were anonymous; students, however, did create their own personal identification numbers for longitudinal tracking purposes. No incentives were offered for participation. Due to the sensitive nature of the items contained in the survey, resource cards with local referral information were made available to students; teachers were informed about the nature of the survey items as well as school and community referral information. Last, students were informed that the survey was voluntary and that they could skip any item they wanted.
21
Please see Appendix A for more information on the W-193 Regional Project.
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Differences in individual school district policies22 regarding parental consent required procuring passive consent in Arizona and Wyoming and active consent in California and Nevada school sites. These differences in consent procedures resulted in a greater than 90% response rate at the Arizona and Wyoming school sites, and approximately 33% rates in Nevada and California23. Data collected from the W-193 regional project are an excellent secondary data source to test the hypotheses presented above. At the theoretical level, these data are appropriate as they were collected using an ecological framework described in the W-193 regional project summary (see Appendix A). Although no specific theories were specified to be tested for the regional project, the selection of measures was consistent with an ecological framework prescribed by Bronfenbrenner’s model of human development. Specifically, a variety of measures encompassing personal, interpersonal, and contextual constructs were assessed. At a more practical level, these data were appropriate for testing the hypothesized pathways, outlined above, for several reasons. First, the four states where the data were collected (Arizona, California, Nevada, and Wyoming) rate among the highest in suicide risk, which has common correlates (e.g., impulsivity) to violence (Hillbrand, 2001). Second, the constructs and items included in the survey parallel those delineated in the proposed model of delinquency. Third, the large sample size (n = 1,286) provided sufficient degrees of freedom to assess the structural model. The large sample size also allowed estimation procedures to be robust against minor deviations from normality. The latter is an important strength of these data given that 22
Passive consent requires that parents be informed that their child is participating in a school-approved research study, information about the nature of the study, and contact information. Parents who did not wish their child to participate in the study could send a note excusing the child from participation. Active consent requires that parents be provided the same information. In order for a child to participate in a study however, parents were required to sign and return the consent form. 23 Estimation-Maximization derived means where compared between active and passive groups, although no significance tests were conducted. No nominal differences were seen in problem behaviors except for higher drug scores among the passive consent group.
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patterns of offending in general populations are subject to skewness resulting from a minority of highly active offenders. PARTICIPANTS Data were collected from a sample of 1,286 eighth grade students from schools in Arizona (30.9%), California (17.5%), Nevada (28.2%), and Wyoming (23.4%). The sample was roughly equally divided by gender with 45.9% male and 54.1% female. Over one-half of the sample (53.3%) was Caucasian, followed by Hispanic (21.8%), Asian (7.8%), Multi-Ethnic (6.2%), Native American (4.7%), and African-American (2.9%) backgrounds. The majority (59.5%) of participants was 14 years of age, whereas 32.9% were 13 years old, 7.2% were 15 years old, and 0.3% was 12 years old. Nearly one-half of the sample (54.1%) reported living with both biological parents, followed by 19.1% who lived with their mothers, 14.3% who lived with their mothers and stepfathers, 4.7% who lived with their fathers, 3.4% who lived with their fathers and stepmothers, 2.2% who lived with other relatives, and 0.8% who lived with foster parents or guardians. Thirty two percent of participants indicated that they spoke a language other than English while at home. STATISTICAL ANALYSES: CONCEPTS, DEFINITIONS, AND PROCEDURES The purpose of this study was to develop a theoretical model of delinquency and test its empirical validity. Specifically, this study examines whether the process by which personal, interpersonal, and contextual variables combine to explain problem behaviors, was supported by the data. An additional interest of this study was determining whether this process (i.e., model) applies similarly across male, female, rural, and urban adolescent populations. The following section describes how structural equation modeling (SEM) was used to answer these questions. The discussion of the statistical procedures used in this study is divided into three sections. The first section describes the four steps required for proper model testing using SEM (Kenny, Kashy, & Bolger, 1998). The second section describes the three phases of analysis required to establish mediated relationship among factors 93
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(Baron & Kenny, 1993). The last section discusses the steps in testing a model for multi-group invariance (Byrne, 2001). SECTION 1: FOUR STEPS TO MODEL TESTING Specification Specification refers to: (a) the presentation of a conceptual (theoretical) model detailing the proposed relationships between factors and (b) the delineation of the variables that comprise these factors. The latter applies to the development of measurement models while the former refers to latent variable models. Figure 3.1 is a graphical specification of a construct in the form of a measurement model. Figure 3.2 is a graphical specification of a latent variable model. Both figures depict the models as they would appear prior to analysis, with all required pathways. However, specification also is an illustration of those relations between factors that are deemed to be nonexistent or mediated by a third factor. A conceptual model, as illustrated in Figure 3.3, shows the purported relationship between latent variable LV 1 and LV 3. In contrast to Figure 3.2, the relationship between LV 1 and LV 3 is not shown as it is mediated by LV 2, and hence does not exist. Figure 3.1 Components of a Measurement Model
The latent variable model for this project was developed and specified in Chapter 2 and illustrated in Figure 2.9. The measurement models that make up the latent variables in the structural model are
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specified in the Measures section below. No additional measurement model figures are provided as they all take the form depicted in Figure 3.1. Figure 3.2 Components of a Latent Variable Model
Identification Following specification, the second step of identification determines whether the proposed model has enough information to be estimated. Specifically, one must calculate whether the number of known values (i.e., data points) meet or exceeds the number of free parameters in the model. In SEM, data points refer to the number of (q) variances and covariances of all of the indicators (k) in the structural model. The number of data points (q) given a specified number of variables (k) is denoted by q = [k(k + 1)]/2. Thus, a measurement model with k = 3 indicators, has q = [3(4)]/2 = 6 data points corresponding to the variances and covariances of the three indicators. However, referring to Figure 3.1, these six data points must be used to calculate the six free parameters in the measurement model, including: the factor loadings for the indicators (three) and the error variances for the indicators (three). Subtracting the number of parameters to be estimated from the number of data points leaves us with zero degrees of freedom. Models
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with zero degrees of freedom are just-identified. Just-identified models have sufficient degrees of freedom to calculate the factor loadings. However, the models lack the necessary degrees of freedom to calculate a chi-square statistic comparing the model of interest against a worse fitting model. In other words, just-identified models cannot be rejected or accepted.24 Figure 3.3 Specification of a Conceptual Model
Models also can be under-identified or over-identified. An underidentified model has fewer than the required data points to estimate the necessary parameters. For example, a model with k = 2 indicator variables will only have q = [2(3)]/2 = 3 data points. Three data points are not sufficient to estimate four parameters: the two factors loadings for the indicators variables and the two error variances for the pair of indicators. On the other hand, an over-identified model has more than the minimum number of data points to estimate the necessary parameters. For example, a model with four indicator variables (k) will have q = [4(5)]/2 = 10 data points. Ten data points leaves us with two degrees of freedom after estimating eight parameters: The four factor loadings for the indicators variables and the four error variances corresponding to those indicators. Identification is calculated after specification and prior to estimating each measurement model (or full structural model).25 In general, additional indicators provide greater degrees of freedom (Marsh, Kit-Tai, Balla, & Grayson, 1998) but do so at a cost of 24
The term “accept” is used here as a failure to reject. In practice, identification for complex LV models is computed by the statistical software prior to analysis. 25
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increased model complexity and reduced model fit (A. Acock, 2004, personal communication, September 23, 2004). To increase the likelihood of adequate model fit, the minimum of three indicators per construct was used for each measurement model of this study. In order to obtain fit indices, all of the (just-identified) measurement models were estimated together, freeing up the necessary degrees of freedom. For further discussion of the benefits and limitations of this approach, please see Appendix C. Estimation Once identification has been determined, the third step of estimating (i.e., analyzing) the structural model can begin. Estimation requires that the model be specified within the statistical analysis program. In the case of a measurement model, this involves associating the indicator items to their respective errors, and to a latent construct as shown in Figure 3.1. In the case of a full latent variable model, this involves specifying the relationships (i.e., pathways or arrows) between the latent constructs in a model as shown in Figure 3.2. The statistical program AMOS 5.0 allows one to specify a model graphically by manually drawing each component of a model. Nevertheless, specification information also can be entered in its final matrix form. After the measurement model (or LV) model is specified, AMOS calculates the variances and covariances of the indicator variables in the model and places them in a matrix. As noted earlier, this matrix represents the data points of structural equation modeling analysis. Using confirmatory factor analysis (CFA), AMOS compares the variance-covariance structure found in the data with the variancecovariance structure specified by the pathways in the hypothesized model. CFA can be contrasted with exploratory factor analysis (EFA). EFA is a procedure that allows for the extraction of factors from an existing data set. EFA, unlike CFA, does not involve an a priori specification of a factor structure, but allows for exploration within the data. Given the research discussed earlier, it was expected that the indicator variables selected would be predictive of the underlying latent constructs. Hence, CFA was the appropriate method for this project. CFA has two major strengths: (a) it is able to measure latent constructs with multiple indicators (and these measures need not be
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based on the same scaling values), and (b) it separates shared variance among the indicator variables from their respective error variances. These two important strengths of CFA allow for improvement in measurement reliability. Improved reliability reduces possible bias, making the estimation of path coefficients and their corresponding tests of statistical significance more accurate (Maruyama, 1998). Altogether, the following measurement models were estimated using CFA: parental monitoring, family conflict, social bond, anger control, internal locus of control, and problem behaviors. Once the estimation process is complete, an evaluation of how adequately the model reflects patterns in the data begins. Evaluation After the factor loadings and parameter values have been estimated, the model must be evaluated to determine if the data support the specified model. To assess the adequacy of measurement or structural models, three types of fit indices, suggested by Jaccard and Wan (1996), are examined: (a) absolute fit indices, (b) relative fit indices, and (c) parsimony fit indices. Absolute fit indices reflect whether the unexplained variance remaining after a model is estimated is statistically significant. These indices address how closely the fitted model parallels a “perfect” model based on the variance/covariance matrix. The difference between the observed patterns in the data and the patterns purported by the structural model is presumed to be asymptotic with a chi-square distribution. To assess the adequacy of the chi-square statistic and its corresponding p-value, Maruyama (1998) and Schumacker and Lomax (1996) suggest that p-values should be non-significant. In this study, two absolute fit indices were used to assess model fit: the χ2 statistic and its corresponding p-value and the chi-square/degrees of freedom (χ2/df) ratio. Chi-square values can be sensitive to sample sizes (Byrne, 2001) such that random differences between the estimated and observed variance-covariance matrices are seen as significant (Rosay, Gottfredson, Armstrong, & Harmon, 2000). Therefore, dividing the χ2 value by the degrees of freedom provides a less biased26 26
Hayduk & Glaser (2000) argue that such sensitivity provided by a large sample size is precisely why large sample sizes are preferred. They add that
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index than a standard chi-square value. Carmines and McIver (1981) and Byrne (1989) suggest that χ2/df ratios of three and below reflect good model fit. In contrast, Rosay et al. suggest that a χ2 /df ratio of five may be too low. The authors add that models whose χ2 /df ratio were above five did not differ substantively from models whose χ2 /df ratios were less than five. Given the large sample size in this study, a χ2 /df ratio of five will be used to assess model fit. Relative-fit indices address how well a particular model fits the data compared to alternative, possible models. Typically, models can be compared to what are referred to as “independence,” “null,” or “worst-fitting” models that only estimate variances from the variance/covariance matrix. In other words, independence models assume no relationships between variables. A condition of no relationship is rarely the case and most alternative models, especially those that are theoretically derived, should compare well against the independence model. In addition to the Independence model, hypothesized models can be compared to alternate conceptual models. Three relative-fit indices were used to compare models in the current study: (a) the normed fit index (NFI; Bentler & Bonett, 1980), (b) the comparative fit index (CFI; Bentler, 1990), and (c) the incremental fit index (IFI; Bollen, 1989). Maruyama (1998) and Schumacker and Lomax (1996) suggest that optimal levels of the NFI, CFI, and IFI approximate .90 or above. Last, parsimony fit indices reflect how well a model combines fit and parsimony. Parsimony fit indices can identify models that account for much variance by leaving few parameters free to vary. These indices are important since good model fit can be achieved by estimating a large number of parameters within a structural model (Maruyama, 1998). To assess model parsimony (i.e., its absolute fit adjusted by a penalty for lack of parsimony), the root mean square error of approximation (RMSEA; Steiger & Lind, 1980) will be examined. MacCallum, Browne, and Sugawara (1996) suggest that RMSEA values of .05 and below reflect good model fit, whereas Jaccard and the onus should be on the researcher to demonstrate that discrepancies between the matrices are random. Rosay et al. (2000) and Mulaik & Millsap (2000) would argue that it is not always possible to determine the cause of the discrepancies. However, one should not discount a “close-fitting” model when other (e.g., relative) fit indicators provide support in favor of the model.
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Wan (1996) contend that RMSEA values below .08 reflect adequate model fit. In sum, the process of testing latent variable models requires that they be specified and identified, then analyzed, and evaluated. Fit indices (χ2/df ratio, NFI, CFI, IFI, and RMSEA) tell us whether a model adequately reflects the patterns in the data. Significant factor loadings however, are key indicators of fit; especially when dealing with just-identified measurement models that provide no additional fit indices or when all measurement models are simultaneously estimated. A non-significant factor loading for an indicator requires the removal of that indicator. When dealing with full structural models, lack of fit suggests that the data fail to confirm patterns hypothesized in the model. At such a juncture, one can re-specify the model by removing non-significant components, re-identify, re-estimate, and reevaluate. After re-specifying a model, however, one is no longer engaging in confirmatory factor analyses but exploratory factor analysis. That is, the purpose is no longer to determine whether the data support the model but to determine what model is actually represented by the data. SECTION 2: TESTING FOR MEDIATION To test for mediation in a model using latent variables, data are analyzed in three phases. First, measurement models are created to test the validity of each latent construct in the LV model. Next, the pathways between pairs of latent constructs are estimated to establish a priori relations. The last step involves estimating the full LV model to determine whether the a priori relations are no longer significant but mediated by a third construct. Phase 1: Measurement Models The first phase of the data analysis is the development of measurement models. Measurement models refer to multiple-item measures that come together conceptually to reflect an underlying construct (i.e., factor). Measurement models are estimated prior to testing the relationships between factors in the overall structural model. This is necessary since the combination of measurement models and the hypothesized relationships between their latent constructs make up the LV model (see Figures 3.1 and 3.2). Without sound latent constructs, the adequacy of the overall model is jeopardized.
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Phase 2: Testing Mediation Once all latent constructs have been estimated and evaluated, one can begin testing for bivariate and mediated relationships between constructs. Figure 3.2 illustrates a potentially mediated relationship between two constructs. To test whether a construct “LV2” mediates the relationship between variables “LV1” and “LV3,” it is necessary to examine the bivariate relation between the focal independent variable (“LV1”) and the dependent variable (“LV3”) (Baron & Kenny, 1986). To test a bivariate relation using AMOS, a latent variable model was set up using two constructs. Graphically, the model was equivalent to Figure 3.2 without the mediating construct “LV2.” A statistically significant parameter (pathway) value would indicate an a priori relationship between constructs and sufficient evidence to test for mediation. In rare cases where the mediator has a negative relationship with the predictor and outcome variable, a statistically significant bivariate relationship between the predictor and outcome variable many not be evident. In such case, the mediator may act as a distorter variable and hide the a priori relation altogether. In this study, several proposed mediators should function as protective factors that should correlate negatively with problem behaviors (outcome) and its risk factors (predictors). It may be the case that a protective factor in this model obfuscates an expected relationship between problem behaviors and one of its risk factors. While no such distortion effects were predicted here, non-significant bivariate relations that fit this scenario were tested for mediation in the subsequent test of the LV model. Any set of statistically significant pathways that did not establish an a priori relation between two constructs, were not interpreted as a mediated relationship. Phase 3: Structural Equation Modeling The same four steps involved in testing measurement models also apply to estimating the full structural model. Following the estimation of all measurement models and the estimation of the bivariate relationships between select constructs whose relationships are hypothesized to be mediated, the full structural model can be estimated using CFA. As noted earlier, CFA is a statistical procedure that blends the logic of regression and factor analysis while avoiding their shortcomings. SEM using CFA has two important strengths: (a) it can test relationships
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among latent constructs that are devoid of measurement error, and (b) it can estimate direct and indirect relationships among these latent constructs (Maruyama, 1998). When faced with missing data, SEM generates a solution based on Full Information Maximum Likelihood (FIML) estimation. By estimating missing values from a data set using FIML, AMOS is able to provide a number of fit indices from which to assess model fit. These fit indices were formerly discussed in the section titled “Evaluation.” SECTION 3: TESTING FOR MULTI-GROUP INVARIANCE Prior to statistically testing whether a hypothesized model is similar across different populations, one must first establish that the model adequately reflects patterns found in the data independently for each group. The purpose of a stricter test of invariance is not to determine if the model fits the data. Rather, the test of invariance examines whether the fit of a model deteriorates as increasing portions of the model are constrained to be equal across groups. Therefore, prior to any multigroup comparisons, a given model must be specified, identified, estimated, and evaluated separately for each group. After establishing that the model appears to adequately represent the data in each group, one can proceed to the stricter test of invariance (Byrne, 2001). When testing for multi-group invariance, the model is re-estimated for each group simultaneously. This first model is equivalent to the separate model-runs conducted for each group. However, unlike the independent group analyses described in the paragraph above, simultaneous estimation across groups yields one set of fit indices. The first invariance model that is estimated allows all parameters to vary freely and its χ2 value is computed. Simultaneous estimation is repeated with increasing amounts of restrictions on the structure of the model. Again, the purpose is not to determine whether the data support the model but whether model fit worsens with additional equality constraints. The second model estimation constrains the factor loadings for each construct as equal and computes its χ2. Since the second model is a nested subset of the first model, its absolute fit index lies along the same χ2 distribution and can therefore be compared. More importantly, the change in χ2 between the unconstrained and constrained model also is distributed along a chi-square distribution. The degrees of freedom
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for the new Δχ2 statistic equals the difference between degrees of freedom in the unconstrained and constrained model. When testing for group invariance, measurement models must be compared first, followed by any necessary bivariate models, and the final LV model. If the Δχ2 for a measurement model is not significant, the factor loadings do not differ significantly across groups and the model is group invariant.27 If one were comparing an LV model, one would proceed to test the parameters connecting the latent variables for invariance. Specifically, one would fix the parameters as being equal across groups and estimate the χ2 value and its corresponding degrees of freedom. The difference in the χ2 value and degrees of freedom between the third and second constrained models is then evaluated as a separate χ2 statistic. A non-significant χ2 value would indicate that the parameter estimates (and thus the LV model) are group invariant. MEASURES The measures described below were derived from a larger set of indicators. For a complete description of the rationale and statistical analyses used to select the final set of items, please refer to Appendix C. Anger Control Anger control was measured using three items from the 8-item Anger Control subscale from the State-Trait Anger Expression Inventory (Spielberger, 1988). Research has demonstrated a negative link between anger control and delinquency such that the ability to control one’s anger is related to decreases in delinquent behaviors. For example, Griffin et al., (1999) found that anger control was negatively related to interpersonal aggression, a form of delinquency. Similar results were reported by Coles, Greene, and Braithwaite (2002). The anger control subscale used for this project measures the extent to which an individual attempts to control the expression of anger. Respondents answered the following questions along a 4-point scale (1 = Almost Never to 4 = Almost Always):
27
While the error variances for the indicators may differ significantly, it is considered too strict a test (Byrne, 2001, 2004).
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I control my temper. I keep my cool. I control my behavior.
Items were positively coded such that high scores represent greater anger control. Reliability analyses using these final three indicators resulted in the following α scores across the male, female, rural, and urban groups: α = .84, .81, .83, and .82, respectively. Family Conflict Family conflict was measured using the three items from the 5-item Conflict subscale of the Moos and Moos Family Environment Scale (FES; Bloom, 1985; see items 131-135 in Appendix F). A growing number of studies have demonstrated the deleterious effect of conflict within the family on adolescents’ externalizing behaviors (GormanSmith, Tolan, Loeber, & Henry, 1998; Shek, 2002). Respondents indicated how strongly they agreed with the following conflict-related statements along a 4-point scale (1 = Strongly Agree to 4 = Strongly Disagree): 1. 2.
We fight a lot in our family. In our family, people sometimes get so angry they throw things. 3. In our family, people sometimes hit each other. The items were slightly modified to read in the present tense and negatively coded such that high scores indicate greater perceived family conflict (consistent with the overall scale direction). Reliability analyses using these final three indicators resulted in the following α scores across the four groups: α = .80, .79, .79, and .80, respectively. Parental Monitoring Parental monitoring measures the extent to which respondents believe their parents monitor their everyday social activities. As such, the scale can be more accurately described as a measure of perceived parental monitoring. Many studies have shown that parental monitoring can have a positive impact on delinquent behaviors. For example, Jacobson and Crockett (2000) and Griffin et al. (2000) reported negative associations between parental monitoring and delinquency. For this
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study, parental monitoring was measured using three items developed by the University of Arizona’s Department of Human Development and Family Studies (K. H. Tepper, personal communication, August 14, 2001). Participants answered the following questions along a 5-point scale (1 = Never to 5 = Always): 1. My parent(s) know where I am after school. 2. When I go out at night, my parents know where I am. 3. I talk to my parent(s) about the plans I have with my friends. All items were positively coded such that high scores represent greater parental monitoring. Reliability analyses showed moderate internal consistency across the four groups: α = .73, .74, .76, and .72, respectively. Negative Peer Influence The construct of peer influence is akin to peer pressure; it measures the extent to which an adolescent feels compelled by his/her peers to engage in behaviors he/she wishes to avoid. Peer influence has been associated with delinquency (Pleydon & Schner, 2001), adolescent drug use (Flannery, Williams, & Vazsonyi, 1999), alcohol use, cigarette smoking, and general problem behaviors (Farrell, Kung, White, & Valois, 2000). Peer influence was measured using a singleitem measure developed by the University of Arizona’s Department of Human Development and Family Studies (Karen Hoffman Tepper, personal communication, August 14, 2001). Negative Peer Influence was thus measured directly and not as a multi-item construct with its own measurement model. Respondents answered the following question along a 5-point scale (1 = Never to 5 = Always): 1. I do things to be more popular with my friends. The item was negatively coded such that high scores represent greater peer influence. Locus of Control Locus of control is defined as a generalized expectancy of internal or external control reinforcement (Rotter, 1966) that extends along a continuum from external to internal. Adolescents with an internal locus of control believe that reinforcement (i.e., outcomes of ones behavior)
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is due to his or her abilities or efforts. Adolescents with an external locus of control believe that reinforcement is due to fate, chance, or some powerful external force. External locus of control has been associated with both victimization (Andreou, 2000) and delinquency (Kumchy & Sayer, 1980; Parrott & Strongman, 1984). Locus of control was assessed with three items from the 40-item Nowicki-Strickland Locus of Control Scale (Nowicki-Strickland, 1973). Respondents answered the following questions along a 4-point scale (1 = Strongly Disagree to 4 = Strongly Agree): 1. 2. 3.
I have little control over the things that happen to me. I often feel helpless in dealing with the problems of life. I believe there is little I can do to change many of the important things in my life. All three items were positively coded (i.e., reverse scored) such that high scores represent an internal locus of control. Reliability analyses showed moderate internal consistency across the male (α = .63), female (α = .68), rural (α = .67), and urban (α = .65) groups. Social Bond The social bond construct describes the extent to which an adolescent: a) feels an attachment to parents, teachers, or peers, b) feels committed to engaging in prosocial activities, c) is involved in prosocial activities, and d) holds beliefs reflecting normative values. Hirschi’s (1964) social control theory, of which social bond has been an important predictor of reduced delinquent activity, suggests that positive social bonds/ties can attenuate adolescent participation in delinquent behaviors. That is, adolescents who are closely tied to their communities are less likely to engage in delinquent activities for fear of being caught by relevant others or disappointing close (i.e., adult) acquaintances. In the current study, positive social bond is defined as the extent to which adolescents feel other individuals (i.e., adults and family members) care about them. Social bond was measured with three items from the 8-item Protective Factors scale from National Longitudinal Study on Adolescent Health (Resnick et al., 1997). The scale measures the
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extent to which participants perceive themselves as being supported28 by parents, teachers, and friends, and these perceptions have been negatively associated with delinquency (Morrison, Robertson, Laurie, & Kelly, 2002). Participants answered the following questions along a 5-point scale (1 = Not at All to 5 = Very Much): 1. 2. 3.
How much do you feel that people in your family understand you? How much do you feel that you and your family have fun together? How much do you feel that your family pays attention to you?
Items were recoded so that high scores represent greater social bond. Reliability analyses showed good internal consistency across the four groups: α = .79, .81, .80, and .81, respectively. Problem Behaviors: Delinquency, Aggression, and Drug Use The problem behaviors measure is comprised of three components: delinquency, aggression, and drug use. To arrive at the three requisite indicators for the problem behavior construct, one indicator was allotted to each (delinquency, aggression, drug use) component. Delinquency was measured using a composite of the first five items of the Resnick et al. (1997) delinquency scale. The scale measures the frequency with which respondents participated in violent/non-violent behaviors over the past 12 months. Responses to these items were made along a 4-point scale (0 = Never, 1 = Once, 2 = Twice, 3 = Three or More Times). The first five items of this scale do not allude to interpersonally aggressive behaviors. These features made these items appropriate for creating a delinquency composite that did not tap into a separate component of aggression. Items were coded so that high scores represent greater participation in delinquent behaviors. The items include the following question stems: 28
The final set if (3) indicators is family-related and may thus reflect a family bond as opposed to a general social bond. Nevertheless, to the extent that a family reflects conventional values, bonding with a family is consistent with Hirschi’s notion that prosocial attachments attenuate participation in problem behaviors.
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Paint graffiti or signs on someone else’s property or in a public place. 2. Deliberately damage property that didn’t belong to you. 3. Take something from the store without paying for it. 4. Run away from home. 5. Go into a house or building and steal something. Aggression was measured using a composite of two items from the Resnick scale and a violence-related item not associated with the Resnick scale. Items numbered “6” and “7” (listed below) involved interpersonally aggressive behaviors and were appropriate for the aggression composite. The third item, which was measured along the same scale as items “6” and “7,” measured how often the respondent actually used a weapon to assault someone. Items include the following question stems: 1. 2.
Threaten to use a weapon to get something from someone. Take part in a fight where a group of your friends was against another group. 3. Used a weapon (e.g., knife, gun, etc.) to threaten or assault someone. Drug use was measured using a composite of five items from a drug use scale developed by Evans (1992). The scale assesses how often respondents used the following drugs in the last 6 months along a 7-point scale (0 = Never, 1 = Once or Twice, 2 = A Few Times, 3 = Once a Month, 4 = Once a Week, 5 = Once a Day, 6 = More Than Once a Day): 1. Beer, wine, liquor, etc. 2. Marijuana, grass, or pot. 3. Other illegal drugs, e.g., crack, cocaine, heroin. 4. Inhalants. 5. Steroids. The composites were coded in the same direction as their internal items such that high scores represent greater participation in problem behaviors. Reliability analyses showed moderate internal consistency for this problem behavior measure across the four groups: α = .80, .77, .73, and .81, respectively.
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Neighborhood Risk Perceived neighborhood risk has been associated with adolescent delinquent behavior in several studies. For example, Griffin et al. (1999) reported that neighborhood risk was positively related to interpersonal forms of aggression, and Pettit, Bates, Dodge, and Meece (1999) linked perceptions of neighborhood risk to lack of adult supervision and externalizing behaviors among adolescents. The current project measured the frequency respondents witnessed violent and non-violent (e.g., drug related) events in their neighborhoods. Specifically, items asked whether participants witnessed: 1.
A person or a group of people threatening or harassing someone? 2. A person or a group of people beating up someone else? 3. Two youth groups fighting? 4. Someone getting shot or stabbed? 5. Someone buying or selling drugs? 6. Someone doing drugs in front of you? To capture the increased risk posed by witnessing multiple neighborhood risk-related events, the six dichotomous variables (i.e., 1 = “Yes” and 0 = “No”) were summed to create a Neighborhood Risk (NR) composite score. The NR composite score has a possible range from 0 to 6, with high scores indicating greater perceived neighborhood risk.
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CHAPTER 4
Critical Findings
All analyses were conducted separately across four groups: male versus female and rural versus urban. At each stage of statistical analyses, the results for each group are presented and compared. The three stages of analysis include: (1) measurement model testing, (2) bivariate model testing, and (3) latent variable model testing. For each stage, model specification, identification, estimation, and evaluation are discussed. ASSUMPTIONS Prior to analyzing the models, several assumptions about the characteristics of the data were examined. Statistical assumptions for conducting CFA are consistent with the assumptions guiding multivariate analyses: data accuracy, sample size, missing data, outliers, univariate and multivariate normality, homogeneity of variance, homogeneity of variance-covariance matrices, and multicollinearity (Tabachnick & Fidell, 1996). A review of the data file, using SPSS Frequencies, showed no inaccurate entries or responses out of the expected range29 across all male, female, rural, and urban data sets. Overall, the sample size for the male (n = 590), female (n = 696), rural (n = 572), and urban (n = 714) subgroups was more than adequate to address the research questions and hypotheses in this study. In addition, the results of Little’s (1988) Test for MCAR did not reject the hypothesis that the data are missing completely at random for 29
The range of expected values for each variable was obtained from the codebook for the Teen Safety Survey Questionnaire.
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the male (χ2 = 96.392, df = 3562, p = 1.000), female (χ2 = 96.392, df = 3562, p = 1.000), rural (χ2 = 82.268, df = 3080, p = 1.000), and urban (χ2 = 101.853, df = 4090, p = 1.000) subgroups. Several cases were identified as outliers while some variables indicated deviations from normality. Nevertheless, these findings were consistent with the variables under study. Problem behaviors such as drug use are not normally distributed. Often, a smaller portion of the population engages in these behaviors and does so with substantial frequency. Removal of statistical (but not theoretical) outliers would reduce the power and precision of the study. Thus, no cases were removed from analysis based on extreme response scores. Variables identified as non-normal showed little improvement and sometimes worsened with square-root transformation. Furthermore, the loss of interpretability that comes with a transformation would only be exacerbated by the highly complex model with multiple levels of mediation. Given these concerns and the expected distributions of these problem behavior variables, no transformed data were retained for subsequent analyses. Instead, greater reliance was given to the FIML estimation procedure to be robust against the aforementioned deviations from normality30. Homoskedasticity, or homegeneity of variance (for grouped data), is the assumption that the variability of scores in the dependent variable is equal across different levels of the grouping variable (Tabachnick & Fidell, 1996). Some variables showed evidence of homoskedasticity across gender and location groupings using the more conservative Levene test. However, the low scores for both gender and location on the more robust F-Max test suggested that corrective transformations were not required. The assumption of homogeneous variance-covariance matrices suggests that an entry in a variance-covariance matrix for one level of a dependent variable is equal to the same entry for another level of the dependent variable. The test of homogeneity of covariance matrices is akin to tests of multi-group invariance in SEM. For example, if the moderator variable of gender in this study is presumed to be the dependent variable, one would compare the variance-covariance matrix 30
Bollen-Stine (1992) bootstrapping procedures used to assess model fit and obtain parameter estimates with non-normal data, could not be used since there was missing data.
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across the different levels of gender (i.e., male and female) to see if they are equal. In multi-group analyses, the variance-covariance matrix for a given sample (e.g., male) is compared to the variance-covariance matrix implied by the hypothesized model. The same step is then repeated for the other gender levels (e.g., female). The difference between the implied variance-covariance matrix and the actual matrix for a given sample is denoted by the χ2 value. The difference between the χ2 values for males and females is tantamount to testing whether the variance-covariance matrix for each sample differs from the matrix of the other sample. Given that multi-group tests are incorporated in the analyses, no test of homogeneity of variance-covariance matrices was performed. The presence of multicollinearity also was assessed. Multicollinearity occurs when two independent (or predictor) variables have a correlation of .70 or above. Correlations among all 48 variables were examined for multicollinearity among the male, female, rural, and urban subgroups. No pair of variables had a correlation greater than or equal to .70. Thus, no variables were eliminated for reasons pertaining to multicollinearity. For a more detailed discussion of these assumptions, please refer to Appendix F. DESCRIPTIVE STATISTICS Table 4.1 lists the means for the four demographic groups in this study. Means were calculated using EM estimation, as opposed to listwise or pairwise estimation. EM was selected for three reasons. First, EM estimation most closely resembles the FIML estimation used to test the hypothesized model. Second, EM estimation allows for an equal number of cell entries in each of the columns with no missing data as missing values are estimated. Listwise estimation would have yielded different cell frequencies for each indicator in Table 4.1. Pairwise estimates would have produced equal cell frequencies at the cost of reduced number of cases. Substituting EM estimation over listwise and pairwise estimation is appropriate for this set of 20 indicators since the missing data for this set of variables is MCAR for male (χ2 = 64.138, df = 667, p = 1.000), female (χ2 = 56.974, df = 766, p = 1.000), rural (χ2 = 46.972, df = 625, p = 1.000), and urban (χ2 = 79.674, df = 823, p = 1.000) subgroups.
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Personal Factors Overall patterns show that estimates of anger control were highest among female and rural students. Males reported more internal locus of control than females while rural and urban respondents’ scores were similar. Interpersonal Factors Family conflict and parental monitoring estimates were highest among female and urban students. These finding suggest family factors may have a stronger impact on females and adolescents living in rural areas. Rural students also reported a greater social bond than urban students. However, rural students also reported more susceptibility to peer influence. The pattern for males was similar to those of rural students, with males reporting a greater social bond along with greater susceptibility to peer influence, compared to females. Contextual Factor Perceived neighborhood risk was highest among females and urban students. These findings are supported national (see Table 2.1) and regional estimates showing females feel increasing less safe at school and are more likely to be victimized by males and other females (Chesney-Lind et al., 2001). Results showing greater perceived risk among urban students are consistent with studies that reveal greater incidences of problem behaviors in urban areas (Farrell et al., 2000; Gottfredson et al., 1996). Problem Behaviors Except for drug use (indicator 18), males reported more problem behaviors than females. Urban students reported more problem behaviors than rural students across all three dimensions (delinquency, aggression, and drug use).
Critical Findings
115
Table 4.1 Means Scores for Indicators, Gender and Location Comparisons Factor
Indicator
Male
Female
Rural
Urban
(n = 590)
(n = 696)
(n = 572)
(n = 714)
Anger Control
1
2.64
2.77
2.80
2.64
2
2.66
2.80
2.82
2.68
3
2.75
2.97
2.94
2.81
4
2.14
2.31
2.16
2.29
5
2.01
2.09
2.00
2.10
6
1.91
1.95
1.91
1.95
7
4.17
4.36
4.32
4.23
8
4.19
4.33
4.24
4.28
9
3.65
3.80
3.82
3.65
10
2.94
2.84
2.88
2.89
11
2.91
2.76
2.80
2.85
12
2.94
2.88
2.92
2.90
13
3.41
3.06
3.27
3.18
14
3.57
3.36
3.50
3.42
15
3.73
3.59
3.66
3.65
16
1.32
1.24
1.23
1.32
17
1.31
1.18
1.21
1.27
18
0.41
0.40
0.37
0.43
19
2.29
1.92
2.14
2.05
20
2.53
2.59
2.51
2.61
Family Conflict
Parental Monitoring
Int. Locus of Control
Social Bond
Problem Behaviors
Neg. Peer Influence Perceived N. Risk
Note: Means were calculated using EM estimation.
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Adolescent Problem Behaviors
MEASUREMENT MODELS Measurement Model Specification The specification of a measurement model involves the delineation of the indicators believed to share an underlying factor of interest. The previous chapter described sets of indicators corresponding to individual measures. For the purpose of analysis, specification for each measurement model purports that the latent construct loads on to (i.e., predicts) each of the three indicators. Adequate specification is evidenced by a statistically significant factor loading for each indicator. In the combined-measurement model, latent constructs are set to covary. Thus, specification of the combined-measurement model would include: a) significant factor loadings and b) correlations between constructs consistent with the direction and magnitude discussed in the presentation of the conceptual model within Chapter 2. Measurement Model Identification All individual measurement models were comprised of three indicators. Therefore, they were just-identified with no available degrees of freedom. To obtain model fit indices, all individual measurement models were incorporated into a combined-measurement model. For a combined-measurement model, the resulting 144 degrees of freedom number were obtained by subtracting the number of distinct parameters to be estimated (86) from the number of distinct sample moments (230). The number of parameters and sample moments were similar across groups. Therefore, the degrees of freedom were identical for the male, female, rural, and urban sub-samples. When testing for invariance, the unconstrained model simultaneously estimates the models for two samples. The concurrent estimation results in twice as many sample moments, estimated parameters, and degrees of freedom. Following the unconstrained model, a series of nested, constrained models are estimated. As a given number of parameters are constrained equally in these nested models, there is an equivalent reduction in the number of parameters to be estimated and an equivalent increase in available degrees of freedom. The unconstrained combined-measurement model had 288 degrees of freedom. The first constrained model, where the factor loadings were fixed as equal, yielded 12 additional degrees of freedom. A second
Critical Findings
117
model with fixed covariances31 had 104 parameters to estimate and 356 degrees of freedom. As noted above, the degrees of freedom were based on the number of variables in the model and not the size of each sample. Therefore, the degrees of freedom for gender-invariance and location-invariance measurement models were identical. Measurement Model Estimation All measurement models32 in this study contained three indicators. Three indicators are sufficient to estimate factor loadings but insufficient to obtain a measure of model fit. An alternative to estimating the fit of individual measurement models is to combine all measurement models into one combined model (See Appendix C for a discussion). The greater number of indicators in this combined model In the combinedprovided additional degrees of freedom.33 measurement model, the constructs for each measure were set to covary. This approach is preferred by researchers (Rosay et al., 2000; Mulaik & Millsap, 2000) who believe that it is more relevant to assess how measures perform all together rather than individually. In other words, measures should be assessed in the manner in which they will appear in the final latent variable model--together. For these reasons, a combined measurement model was estimated for each gender and location subgroup. Following an assessment of adequate model fit, the combined measurement model was tested for invariance across gender and location groupings. Measurement Model Evaluation Measurement models which meet the following four criteria were interpreted as having an acceptable fit: a) χ2 /df ratio less than or equal 31
Following the constraining of the factor loadings, the intercept corresponding to the factor loadings and the means of the latent constructs were constrained to be equal across gender. 32 Neighborhood Risk and Negative Peer Influence were single-item measures that were directly observed. 33 Recall that the data points in SEM are the variances and covariances of the indicators. As the number of indicators increases, the number of entries in the variance-covariance matrix increases by [k(k+1)]/2 where k=number of variables.
118
Adolescent Problem Behaviors
to five, b) NFI, CFI, and IFI values greater than or equal to .90, c) RMSEA values at or below .08, and d) all factor loadings are statistically significant. Measurement models were interpreted as invariant if factor loadings did not significantly differ across groups. Earlier practices (Benson, 1987; Nunnally, 1978; Camines & Zeller, 1979; Pedhauzer & Schmelkin, 1991) required that error variances also be similar across groups. More recently, researchers have argued this criterion is too stringent34 (Byrne, 2001, 2004; Rosay, et al., 2000). The covariances between constructs also were examined to determine if they were in the expected direction and magnitude. Results for Gender Table 4.2 shows the model fit for all four groups prior to conducting any tests of invariance. Results show that the combined measurement model for males had a statistically significant chi-square value [χ2 (144) = 211.124, p< .001] suggesting that the patterns in the data differed significantly from the model. When the χ2 was adjusted for sample size, however, its χ2 /df ratio (1.466) was below the maximum of five. The low χ2 /df ratio suggests that the model is not necessarily misspecified, but may have reflected sensitivities to small variations in the data. All relative fit indices were above the acceptable limit of .90, indicating a good fitting model. In addition, the low score on the RMSEA (.028) suggested that good model fit was obtained via parsimony and not a result of having a complex model with many estimated parameters. Table 4.3 shows the standardized and unstandardized factor loadings for the six latent constructs across the four groups. A standardized factor loading that has an exact value of one denotes that its indicator was used to set the scale for that construct. All factor loadings among males were significant at the p < .001 level. Factor 34
Rosay et al. (2000) emphasize that error variables in congeneric (i.e., factor analytic) measurement models are comprised of random error and specific error variance, a reliable portion of a variable that the construct simply does not account for. Classical test theory however, defines reliability in relation to random error and not a combination of specific and random error. The authors add that statistical programs cannot measure specific error variance. Therefore, the expectation of equal error variances is too stringent.
Critical Findings
119
loadings above .600 and below .950 were acceptable (Bagozzi & Yi, 1988). All measures in Table 4.3 had adequate factor loadings above .6 for both gender subgroups. Table 4.4 lists the percentage of variance for each indicator that is accounted for by its corresponding latent construct. A percentage below 50 suggests that a different factor may better predict that indicator. Results show that the indicators for internal locus of control had the lowest percentage of explained variance. These findings are consistent with the EFA results presented in Appendix F. Given that these items were the (theoretically and statistically) strongest available indicators of locus of control, no indicators were removed. However, the low percentage of explained variance for this measure will remain a potential limitation of this study. Last, no factor loadings were recorded for perceived neighborhood risk and negative peer influence as these were single observed variables. Results for female respondents were similar to those for males. The chi-square difference test was significant while the χ2 /df ratio was well within the acceptable boundaries. Relative fit indices also indicated adequate model fit and parsimony. All indicators had loadings above .6 and all were statistically significant at the p <.001 level. Aside from the issues related to the locus of control indicators discussed above, latent constructs accounted for over 50% of the variance for the majority of indicators. Given the similar results among the male and female sub-samples, the combined measurement model was tested for invariance across gender. Table 4.5 lists the results for the series of nested gender measurement models. The Unconstrained model is the equivalent of the boys’ and girls’ model estimated simultaneously. The Measurement Weights model is a nested model where the factor loadings have been constrained as equal for both male and female samples. The Measurement Intercepts model contains all of the constraints of the previous model in addition to constraining the intercepts in the equations for predicting measured variables. The Structural Means model has the constraints of the previous model while constraining the means of the latent constructs as equal. The Structural Covariances model further constrains the covariances between constructs to be equal. The Measurement Residuals constrains the error terms as equal across groups; however, this requirement is now regarded as too strict for establishing invariance.
Table 4.2 Model Fit Prior to Tests of Invariance Parameters
χ2
df
χ2 /df
NFI
IFI
CFI
RMSEA
Male
86
211.124***
144
1.466
.944
.982
.981
.028
Female
86
336.619***
144
2.338
.928
.958
.957
.044
Rural
86
291.894***
144
2.027
.924
.960
.959
.042
Urban
86
305.942***
144
2.125
.934
.964
.964
.040
Model
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, * p < .05, ** p < .01, *** p < .001
Table 4.3 Standardized and Unstandardized Factor Loadings by Group Boys (n = 590) Factor Anger Control
Family Conflict
Parental Monitoring
Girls (n = 696)
Rural (n = 572)
Urban (n = 714)
Indicator
β
b
β
b
β
b
β
b
1
.705
.863
.723
.940
.713
.883
.716
.915
2
.835
1.000
.773
1.000
.818
1.000
.791
1.000
3
.834
.981
.813
1.027
.822
.988
.825
1.025
4
.754
.891
.770
.917
.774
.902
.751
.905
5
.798
1.000
.775
1.000
.799
1.000
.776
1.000
6
.729
.904
.683
.856
.654
.804
.744
.935
7
.661
1.052
.648
1.030
.691
1.013
.619
.904
8
.761
1.000
.780
1.000
.829
1.000
.745
1.000
9
.698
.832
.733
.828
.684
.686
.744
.941
(continued on next page)
Table 4.3 Standardized and Unstandardized Factor Loadings by Group Boys (n = 590) Factor Int. Locus of Control
Social Bond
Problem Behaviors
(continued)
Girls (n = 696)
Rural (n = 572)
Urban (n = 714)
Indicator
β
b
β
b
β
b
β
b
10
.629
1.090
.627
1.037
.660
1.140
.605
.991
11
.618
1.000
.611
1.000
.601
1.000
.632
1.000
12
.627
1.123
.607
1.046
.654
1.179
.581
.983
13
.830
1.000
.837
1.000
.847
1.000
.827
1.000
14
.730
.945
.773
.970
.760
.959
.752
.961
15
.685
.904
.705
.891
.680
.864
.717
.943
16
.880
1.000
.776
1.000
.789
1.000
.856
1.000
17
.777
.929
.652
.777
.628
.831
.775
.902
18
.695
1.089
.822
1.630
.739
1.396
.755
1.259
β – standardized regression weight b – unstandardized regression weight
Critical Findings
Int. Locus of Control Indicator 10 Indicator 11 Indicator 12
Parental Monitoring Indicator 7 Indicator 8 Indicator 9
Family Conflict Indicator 4 Indicator 5 Indicator 6
Anger Control Indicator 1 Indicator 2 Indicator 3
.469 .534 .688
.393 .382 .396
.488 .579 .437
.532 .637 .569
.695 .697 .497
Male
.675 .425 .603
.497 .597 .700
.369 .374 .393
.538 .608 .420
.466 .600 .593
.661 .598 .523
Female
.546 .394 .623
.462 .578 .718
.428 .362 .436
.468 .687 .477
.427 .639 .598
.675 .669 .508
Rural
.570 .601 .733
.514 .566 .683
.338 .399 .366
.553 .555 .383
.553 .603 .565
.681 .626 .512
Urban
123
Social Bond Indicator 13 Indicator 14 Indicator 15
.484 .603 .774
Table 4.4 Percentage of Variance in Each Indicator Explained by Its Respective Factor, Across Four Groups
Problem Behaviors Indicator 16 Indicator 17 Indicator 18
The Saturated and Independence models are the extreme models in a nested series. The Saturated model assumes all parameters are correlated thus saturating all possible relationships between all
124
Adolescent Problem Behaviors
components of a model. Since the model is saturated, it often has no degrees of freedom, thus precluding the computation of absolute fit indices. However, given the saturation, goodness of fit (e.g., CFI, INI, IFI) indices often garner perfect scores of 1.0. The Independence or null model assumes there are no relationships between variables. As such, the null model has the fewest parameters to estimate, the most degrees of freedom available, the worst absolute (e.g., χ2 /df ratio) fit index, and the lowest goodness of fit scores (.000). For the purposes of testing measurement invariance, one need only compare the Unconstrained model to the Measurement Weights model. However, in order to explore the covariances between groups, the invariance of the Structural Covariance model also was examined. The ∆χ2 /∆df column in Table 4.6 lists the difference in χ2 and degrees of freedom between the Unconstrained and subsequent models. Since these models are nested, their difference scores also are distributed along a χ2 distribution. If one assumes that the Unconstrained model is correct, a non-significant ∆χ2 score distributed along ∆df degrees of freedom indicates that adding the additional constraints did not impact model fit. Therefore, the parameters that were constrained for that given model can be viewed as invariant. Results listed in Table 4.5 show that while the χ2 for the Unconstrained model was statistically significant, the χ2 /df ratio was within the acceptable limit of five. These results were similar for all subsequent models. Relative and parsimony indices indicated adequate model fit with the exception of NFI scores slightly under .90 for the Structural Covariances and Measurement Residuals models. These results suggest that all35 of the models adequately reflect the data. When testing for invariance, however, one is interested in the change in model fit caused by increasing model constraints. Table 4.6 delineates the ∆χ2 and ∆df between the Unconstrained model and subsequent nested models. The last column (p) of Table 4.6 shows that each additional parameter constraint resulted in a significant impact on model fit. Therefore, one must conclude that the combinedmeasurement model is not invariant across gender.
35
This statement excludes the NFI scores for the Structural Covariances and Measurement Residuals models.
Table 4.5 Results of Model for Fit from Test of Gender Invariance Parameters
χ2
df
χ2 /df
NFI
IFI
CFI
RMSEA
Unconstrained
172
547.580
288
1.901
.935
.968
.968
.026
Measurement Weights
160
597.694
300
1.992
.929
.964
.963
.028
Measurement Intercepts
142
724.576
318
2.279
.914
.950
.949
.032
Structural Means
140
763.528
320
2.386
.910
.946
.945
.033
Structural Covariances
104
871.445
356
2.448
.897
.936
.936
.034
Measurement Residuals
86
975.668
374
2.609
.885
.926
.925
.035
Saturated Model
460
.000
0
---
1.000
1.000
1.000
.122
Independence Model
40
8468.278
420
20.163
.000
.000
.000
.026
Model
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, * p < .05, ** p < .01, *** p < .001
126
Adolescent Problem Behaviors
Results for Location Similar to the male and female samples, the combined measurement models for rural and urban respondents had a statistically significant chi-square value while its χ2 /df ratio was no larger than 2.7 (see Table 4.7). Both models showed adequate fit and parsimony with relative fit indices scores above .90 and RMSEA scores well below .08. Table 4.6 Results of Nested Model Comparisons from Test of Gender Invariance ∆χ2
∆df
p
---
---
---
Measurement Weights
50.114
12
< .001
Measurement Intercepts
126.882
18
< .001
Structural Means
38.952
2
< .001
Structural Covariances
107.917
36
< .001
Measurement Residuals
104.223
18
< .001
Model Unconstrained
∆χ2 = change in χ2 ∆df = change in degrees of freedom
In addition to the lack of explained variance for the locus of control indicators, one locus of control indicator (#12) had a loading below .60. Despite the performance of this indicator, the locus of control measure was not modified. While retaining this item would increase the likelihood of measurement non-invariance, this cost may be offset by: a) the additional variance provided by having an additional indicator in the locus of control measure and b) having a similar locus of control measure across the four sub-samples. All factor loadings across rural and urban sub-samples were statistically significant at the p <.001 level while latent constructs accounted for over 50% of the variance for the majority of indicators. Given the similar results across groups, the combined measurement model was tested for invariance across location. Results listed in Table 4.7 show that all (Unconstrained and nested) models had a significant χ2 but a χ2 /df ratio less than 2.3. Relative and parsimony indices also showed adequate model fit indicating that all models
Critical Findings
127
adequately reflected the data in the samples. Table 4.8 lists the ∆χ2 and ∆df between the Unconstrained model and subsequent nested models for the location sub-samples. The last column (p) of Table 4.8 shows that with the exception of the Structural Means model, each additional parameter constraint resulted in a significant impact on model fit. Therefore, one must conclude that the combined-measurement model is not invariant across location. Variance-Covariance Matrix The variance-covariance matrixes of all latent constructs in this study are displayed in Table 4.9 and Table 4.10. Covariances among the measures were generally of the expected valence and magnitude. There were some exceptions that raised concerns. In particular, the relationships between problem behaviors and the personal level mediators of anger control and locus of control were small for all four sub-samples. In addition, negative peer influence appears to have weak associations with all other measures except perceived neighborhood risk. The direction of the relationship differed among location subgroups, however. For the rural sample, the covariance between negative peer influence and neighborhood risk was positive (r = .184), but it was negative for the urban sample (r = -.197). Similar differences occurred between social bond and negative peer influence across the four sub-samples. While the direction of the relationships may reflect moderated differences by sample, the overall low covariance scores are not consistent with the literature on the strong relationship between peers and problem behaviors. BIVARIATE MODELS Bivariate Model Specification The specification of the bivariate models was discussed in Chapter 2. The pathways labeled Path 1 through Path 7 in Figure 2.8 represent preexisting relationships necessary for establishing mediation (Baron & Kenny, 1986). An adequately specified bivariate model is evidenced by a statistically significant parameter estimate in a model that meets the four fit criteria discussed in Chapter 3.
Table 4.7 Results of Nested Model Comparisons from Test of Location Invariance Parameters
χ2
df
χ2 /df
NFI
IFI
CFI
RMSEA
Unconstrained
172
598.122***
288
2.077
.930
.962
.962
.029
Measurement Weights
160
622.686***
300
2.076
.927
.961
.960
.029
Measurement Intercepts
142
665.223***
318
2.092
.922
.958
.957
.029
Structural Means
140
668.207***
320
2.088
.922
.958
.957
.029
Structural Covariances
104
785.733***
356
2.207
.908
.947
.947
.031
Measurement Residuals
86
845.709***
374
2.261
.901
.942
.942
.031
Saturated Model
460
.000
0
---
1.000
1.000
1.000
.123
Independence Model
40
8513.796***
420
20.271
.000
.000
.000
.029
Model
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, ∆χ2 /∆df = change in χ2 divided by the change in degrees of freedom between nested models. * p < .05, ** p < .01, *** p < .001
Critical Findings
129
Bivariate Model Identification The bivariate models estimated the relationship between two measures. In general, model identification was similar and differed only if a single item indicator was included in the model. All latent constructs were comprised of three indicators while the negative peer influence and neighborhood risk measures were both single indicators. Models that included a single-item measure (Paths 1, 3, 4, 5, and 7) had 14 data points and 12 parameters to estimate, yielding two degrees of freedom. Models with two latent variables had 27 data points and 19 parameters to estimate, resulting in 8 degrees of freedom. All models had sufficient degrees of freedom to calculate the parameter estimates and provide model fit indices. Bivariate Model Estimation Eight bivariate models were estimated, each representing a necessary pathway for establishing mediation. In keeping with the multi-group analyses, bivariate models were tested separately for the male, female, rural, and urban sub-samples. Tables 4.11 through Table 4.12 contain the results of the model estimation for each respective sub-sample. Table 4.8 Results of Nested Model Comparisons from Test of Location Invariance ∆χ2
∆df
p
---
---
---
Measurement Weights
24.564
12
.017
Measurement Intercepts
42.537
18
.001
Structural Means
2.984
2
.225
Structural Covariances
117.526
36
< .001
Measurement Residuals
59.976
18
< .001
Model Unconstrained
2
2
∆χ = change in χ ∆df = change in degrees of freedom
Table 4.9 Variance-Covariance Matrix of Factors in Problem Behaviors Model, Arranged by Gender Male (n = 590) Anger Control Family Conflict Parental Monitoring Neg. Peer Influence a Int. Locus of Control Social Bond Problem Behaviors Neighborhood Risk a
Anger Family P a r e n t a l Control Conflict Monitoring
Neg. Peer Influence
Locus of Control
Social Bond
Problem Behaviors
Neighborhood Risk
.699 -.249
.619
.208
-.247
.658
.045
-.042
.098
1.222
.058
-.142
.077
-.019
.326
.189
-.333
.369
.005
.137
.766
-.082
.113
-.196
-.111
-.059
-.117
.226
-.087
.238
-.246
-.400
-.134
-.018
.375
(continued on the next page)
3.550
Table 4.9 Variance-Covariance Matrix of Factors in Problem Behaviors Model, Arranged by Gender (continued)
Female (n = 696) Anger Control Family Conflict Parental Monitoring Neg. Peer Influence a Int. Locus of Control Social Bond Problem Behaviors Neighborhood Risk a a
Anger Family P a r e n t a l Control Conflict Monitoring
Neg. Peer Influence
Locus of Control
Social Bond
Problem Behaviors
Neighborhood Risk
.559 -.266
.626
.209
-.331
.664
-.012
-.002
.054
1.075
.125
-.145
.130
-.018
.341
.289
-.471
.420
-.047
.234
.914
-.075
.103
-.155
-.085
-.048
-.119
.101
-.111
.395
-.336
-.449
-.072
-.032
.299
Negative Peer Influence and Neighborhood Risk were not latent factors but were measured directly.
3.344
Table 4.9 Variance-Covariance Matrix of Factors in Problem Behaviors Model, Arranged by Location R u r a l (n = 572) Anger Control Family Conflict Parental Monitoring Neg. Peer Influence a Int. Locus of Control Social Bond Problem Behaviors Neighborhood Risk a
Anger Family P a r e n t a l Control Conflict Monitoring
Neg. Peer Influence
Locus of Control
Social Bond
Problem Behaviors
Neighborhood Risk
.643 -.279
.628
.229
-.275
.767
-.044
.147
-.083
1.194
.100
-.161
.112
-.086
.322
.261
-.435
.413
-.089
.186
.812
-.088
.090
-.174
.031
-.059
-.119
.099
-.137
.378
-.277
.184
-.156
-.424
.269
(continued on the next page)
3.013
Table 4.9 Variance-Covariance Matrix of Factors in Problem Behaviors Model, Arranged by Location (continued)
Urban (n = 714) Anger Control Family Conflict Parental Monitoring Neg. Peer Influence a Int. Locus of Control Social Bond Problem Behaviors Neighborhood Risk a a
Anger Control
Family Conflict
Parental Monitoring
Neg. Peer Influence
Locus of Control
Social Bond
Problem Behaviors
Neighborhood Risk
.607 -.228
.621
.200
-.290
.619
-.033
.002
-.003
1.158
.084
-.137
.092
-.087
.355
.210
-.391
.365
.060
.202
.886
-.070
.116
-.184
.015
-.044
-.109
.202
-.056
.280
-.308
-.197
-.062
-.431
.378
Negative Peer Influence and Neighborhood Risk were not latent factors but were measured directly.
3.776
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Adolescent Problem Behaviors
Bivariate Model Evaluation Results for Gender Among males, all bivariate models had adequate fit with nonsignificant χ2 values or χ2 /df ratio below five. Referring to Table 4.11 and Figure 2.8, the models representing Path 1 and Path M recorded non-significant parameter estimates. For the Path 1 model, these results imply that neighborhood risk was not related to anger control. For the latent variable model, these results suggest that a statistically significant relationship between neighborhood risk and family conflict (Path A), and between family conflict and anger control (Path B), does not establish that family conflict mediates the relationship between neighborhood risk and anger control. The implication is similar for the non-significant Path M model. If Path H and Path I are significant in the latent variable model, it will not have been established that internal locus of control mediates the preexisting relationship between negative peer influence and problem behaviors.36 Results were similar among the female sub-sample. All models except the Path 1 model had adequate model fit. Nevertheless, the Path 1 model also showed a non-significant relationship between neighborhood risk and anger control. The same implications drawn for the male sub-sample also can be drawn for the female sub-sample regarding Path 1. In contrast to the male sub-sample, the Path 5 model showed a non-significant relationship between neighborhood risk and internal locus of control. If Path F and Path H are significant in the latent variable model, it will not have been established that negative peer influence mediates the relationship between neighborhood risk and internal locus of control. Path M was significant and the model was adequate; however, the relationship was negative (opposite from expected) and very weak (b = -.010). Given these results, this relationship is accepted with caution. 36
Baron and Kenny (1986) do acknowledge that a bivariate relationship may appear non-significant if the mediator is of the opposite valence as the predictor and outcome variable. In such cases, a mediator behaves as a disruptor variable that nullifies any apparent relationship the predictor shares with the outcome variable. While no such predictions are made of the mediators in this study, it does not preclude the possibility that such disruptions may be in effect.
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135
Results for Location In the rural sample, all bivariate relationship paths were statistically significant (see Table 4.11). The χ2 and χ2 /df ratio, however, were not adequate for the Path 1 and Path 4 models. These results suggest that the relationship depicted in the Path 1 and Path 4 models do not fit the data and the significant factor loadings should be treated as potentially dubious. The implications of a non-significant Path 1 model have been addressed above. For Path 4, a non-significant relationship between neighborhood risk and problem behaviors limits the interpretation of latent variable model findings. Specifically, if Path J and Path M are significant in the latent variable model, it will not have been established that social bond mediates the relationship between neighborhood risk and internal locus of control. In the urban sub-sample, the χ2 and χ2 /df ratios for all of the bivariate models were adequate (see Table 4.11). The Path 1, Path 5, and Path M models, however, recorded non-significant relationships. As noted above, these results limit the ability to interpret family conflict and negative peer influence as mediators if their adjoining pathways are significant. Overall, few differences were discerned among the gender and locations subgroups. The relationship between negative peer influence and problem behaviors (Path M) was not established among the male and urban sub-samples. Among female and urban samples, the relationship between neighborhood risk and internal locus of control (Path 5) also was not established. For the rural sample, the relationship between neighborhood risk and problem behaviors (Path 4) was significant. This relationship was not accepted as the model was not adequate (i.e., χ2 /df = 5.2). Last, the relationship, denoted by Path 1, between neighborhood risk and anger control was not supported in any of the four samples.
Table 4.11 Estimated Direct Relationships Between Constructs Prior to Test of Mediation, Male and Female Samples
Path 1 (-)
NR Î AC
-.025
-.055 n.s.
2.950 n.s.
2
1.475
.996
.999
.999
RMSE A .028
Path 2 (+)
FC Î ProBeh
.172
.297***
5.420 n.s.
8
.678
.995
1.002
1.000
.000
Path 3 (-)
NR Î PM
-.074
-.161***
.235 n.s.
2
.117
.999
1.004
1.000
.000
Path 4 (+)
NR Î ProBeh
.105
.424***
.862 n.s.
2
.431
.999
1.002
1.000
.000
Path 5 (-)
NR Î ILOC
-.039
-.127*
3.658 n.s.
2
1.829
PM Î ILOC
.109
.164**
6.864 n.s.
8
.858
.993 1.002
.993 1.000
.038
Path 6 (+)
.985 .989
Path M(+)
NPI Î ProBeh
.037
.088 n.s.
6.100*
2
3.050
.990
.993
.993
.059
Path 7 (+)
SB Î ILOC
.181
.271***
4.706 n.s.
8
.588
.994
1.004
1.000
.000
Path
Model (Male)
b
β
χ2
df
χ2/df
NFI
IFI
CFI
(continued on next page)
.000
Table 4.11 Estimated Direct Relationships Between Constructs Prior to Test of Mediation, Male and Female Samples (continued) Path
Model (Male)
b
β
χ2
df
χ2/df
NFI
IFI
CFI
RMSEA
Path 1 (-)
NR Î AC
-.033
-.081 n.s.
11.318**
2
5.659
.984
.987
.987
.082
Path 2 (+)
FC Î ProBeh
.162
.405***
17.496*
8
2.187
.986
.993
.992
.041
Path 3 (-)
NR Î PM
-.102
-.224***
3.399 n.s.
2
1.700
.994
.997
.997
.032
Path 4 (+)
NR Î ProBeh
.089
.513***
1.887 n.s.
2
.943
.998
1.000
1.000
.000
Path 5 (-)
NR Î ILOC
-.020
-.063 n.s.
2.405 n.s.
2
1.203
.991
.998
.998
.017
Path 6 (+)
PM Î ILOC
.187
.270***
10.706 n.s.
8
1.338
.987
.997
.997
.022
Path M(+)
NPI Î ProBeh
-.010
-.033*
1.030 n.s.
2
.515
.998
1.002
1.000
.000
Path 7 (+)
SB Î ILOC
.250
.417***
9.637 n.s.
8
1.205
.991
.998
.998
.017
b = unstandardized beta coefficient, β = standardized beta coefficient, CR = critical ratio, NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, NR = Neighborhood Risk, PM = Parental Monitoring, SB = Social Bond, FC = Family Conflict, NPI = Negative Peer Influence, ProBeh = Problem Behaviors, AC = Anger Control, ILOC = Internal Locus of Control, *p < .05., **p < .01., ***p < .001 n.s. = not statistically significant.
Table 4.12 Estimated Direct Relationships Between Constructs Prior to Test of Mediation, Male and Female Samples df NFI IFI CFI RMSEA Path Model (Male) b χ2 χ2/df β Path 1 (-)
NR Î AC
-.044
-.095*
13.687***
2
6.843
.979
.982
.981
.101
Path 2 (+)
FC Î ProBeh
.147
.361***
3.851 n.s.
8
.481
.996
1.004
1.000
.000
Path 3 (-)
NR Î PM
-.095
-.183***
3.509 n.s.
2
1.755
.993
.997
.997
.036
Path 4 (+)
NR Î ProBeh
.090
.494***
10.401**
2
5.200
.980
.984
.984
.086
Path 5 (-)
NR Î ILOC
-.050
-.153**
5.664 n.s.
2
2.832
.978
.986
.985
.057
Path 6 (+)
PM Î ILOC
.142
.225***
3.131 n.s.
8
.391
.996
1.007
1.000
.000
Path M(+)
NPI Î ProBeh
.034
.108*
2
3.981
.981
.986
.985
.072
Path 7 (+)
SB Î ILOC
.226
.362***
7.961* 12.715 n.s.
8
1.589
.985
.994
.994
.032
(continued on next page)
Table 4.12 Estimated Direct Relationships Between Constructs Prior to Test of Mediation, Rural and Urban (continued) Samples df χ2/df NFI IFI CFI RMSEA Path Model (Male) b χ2 β Path 1 (-)
NR Î AC
-.015
-.038 n.s.
2.178 n.s.
2
1.089
.997
1.000
1.000
.011
Path 2 (+)
FC Î ProBeh
.176
.319***
10.228 n.s.
8
1.278
.993
.999
.998
.020
Path 3 (-)
NR Î PM
-.084
-.201***
1.337 n.s.
2
.669
.997
1.001
1.000
.000
Path 4 (+)
NR Î ProBeh
.100
.435***
3.683 n.s.
2
1.842
.996
.998
.998
.034
Path 5 (-)
NR Î ILOC
-.016
-.052 n.s
2.218 n.s.
2
1.109
.991
.999
.999
.012
Path 6 (+)
PM Î ILOC
.141
.196***
11.580 n.s.
8
1.448
.984
.995
.995
.025
Path M(+)
NPI Î ProBeh
.013
.031 n.s.
5.932 n.s.
2
2.966
.993
.995
.995
.053
Path 7 (+)
SB Î ILOC
.230
.358***
8.390 n.s
8
1.049
.992
1.000
1.000
.008
b = unstandardized beta coefficient, β = standardized beta coefficient, CR = critical ratio, NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, NR = Neighborhood Risk, PM = Parental Monitoring, SB = Social Bond, FC = Family Conflict, NPI = Negative Peer Influence, ProBeh = Problem Behaviors, AC = Anger Control, ILOC = Internal Locus of Control, *p < .05., **p < .01., ***p < .001 n.s. = not statistically significant.
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Adolescent Problem Behaviors
LATENT VARIABLE MODELS Latent Variable Model Specification The specification of the latent variable model was discussed in Chapter 2 and illustrated in Figure 2.9. The pathways labeled Path A through Path L in Figure 2.9 represent the hypothesized mechanisms through which contextual, interpersonal, and personal factors combine to explain problem behaviors. An adequately specified latent variable model is evidenced by statistically significant parameter estimates (Path A through Path L) in a model that meets the three additional fit criteria discussed above. Latent Variable Model Identification All latent variable models were over-identified with sufficient degrees of freedom to calculate all parameter estimates and provide model fit indices. The identification for the latent variable models was calculated in a fashion similar to that of the combined measurement models. The available degrees of freedoms for the latent variable models were calculated by subtracting the number of parameters to be estimated from the number of available data points. The latent variable models in this study had 230 data points, 78 estimated parameters, and 152 degrees of freedom. Each sub-sample tested the same model and thus had an equal number of degrees of freedom. Latent Variable Model Estimation Prior to any test of invariance, the latent variable model was tested separately across the four sub-samples. Latent variable models that met the following four criteria were viewed as having adequate fit: a) χ2 /df ratio less than or equal to 5.0, b) NFI, CFI, and IFI values greater than or equal to .90, c) RMSEA values at or below .08, and d) statistically significant hypothesized factor loadings and parameter estimates. The tests for latent variable model invariance were similar to those for measurement models. After determining that the Unconstrained model had adequate fit, the χ2 values for a series of increasingly
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141
constrained models were compared. Beginning with the Unconstrained model, the χ2 score and degrees of freedom of a given model were subtracted from the χ2 score and degrees of freedom of a more constrained model. The difference in the χ2 values (∆χ2) was then used as a χ2 statistic distributed along ∆df degrees of freedom. Assuming that the initial Unconstrained model is true37, a statistically significant ∆χ2(∆df) would indicate that constraining a given set of parameters as equal between two samples resulted in a statistically significant change in the fit of the model. The tests of measurement invariance and structural invariance share several constrained models. Recall that the test of measurement invariance compared the Unconstrained latent variable model to the Measurement Weights, Measurement Intercepts, Structural Means, Structural Covariances, and Measurement Residuals models. In addition to the five constrained models, the test for structural invariance includes three constrained models: Structural Weights, Structural Intercepts, and Structural Residuals. The Structural Weights and the Structural Intercepts models fix the regression weights and intercepts for the factors as equal across groups. The Structural Residuals model further constrains the error variances of (endogenous) latent measures as equal across groups. Similar to the assumption of equal measurement residuals, the assumption of equal structural residuals is considered too strict to establish structural invariance. For this study, equivalent measurement and structural weights were the main focus for invariance testing. If the latent constructs and the pathways between constructs were equal across the different sub-samples, the model was interpreted as invariant across gender and location.
37
True refers to adequately reflecting the data.
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Adolescent Problem Behaviors
LATENT VARIABLE MODEL EVALUATION Results for Gender The results of the estimated model across all samples are listed in Table 4.13. This initial analysis was used to determine whether the model had obtained adequate fit for each sub-sample. Results of parameter estimates also were examined to determine whether: a) any hypothesized relationships was non-significant, and b) any pattern of non-significant relationships was similar across groups. The model for the male sub-sample reported adequate fit with a χ2 /df ratio of 2.51, a RMSEA score of .05, and IFI and CFI values above .90. The NFI value of .89 was below the specified threshold of .90 for relative fit indices. The fit indices for the female sub-sample were less adequate than those of the male sample. However, all model fit indices (except the NFI) were within the range of acceptable scores. The fourth criteria to determine whether a model had adequate fit required establishing statistically significant factor loadings and parameter estimates. To facilitate the comparison of parameter estimates between gender models, only statistically significant pathways were included in Figures 4.1 and 4.2. Several patterns emerged in the results. First, neither sample replicated all of the significant parameters specified in the hypothesized latent variable model. Second, intra-psychic (personal) protective factors among males did not mediate any relationships between problem behaviors and the other constructs in the model. Anger control and internal locus of control represent the two personal, protective factors in the model. Among females, anger control appeared to mediate the relationship between family conflict and problem behaviors. Among males and females, internal locus of control was not related to problem behaviors and thus did not mediate any other factors. At the interpersonal level, family-related factors had a significant relationship with problem behaviors. Family conflict and negative peer influence represent the two interpersonal risk factors in the model while parental monitoring and social bond represent the two interpersonal protective factors. As predicted, parental monitoring was significantly
Critical Findings
143
related to problem behaviors for both genders. On the other hand, the relationship between family conflict and problem behaviors was mediated by parental monitoring among males. Among females, the relationship between family conflict and problem behaviors appeared to be mediated by parental monitoring and anger control. Negative peer influence and social bond were related to internal locus of control in both gender sub-samples. Neither measure was directly related to problem behaviors or indirectly through internal locus of control.38 At the contextual level, neighborhood risk was significantly related to family conflict and social bond, as predicted. On the other hand, neighborhood risk was not related to negative peer influence, as had been predicted. Another unpredicted relationship occurred between neighborhood risk and parental monitoring. Indeed, neighborhood risk and problem behaviors shared a direct relationship, a partially-mediated relationship via parental monitoring, and double-mediated relationship via family conflict and parental monitoring. When compared to the hypothesized model depicted in Figures 2.7 and 2.8, Figures 4.1 and 4.2 denote the extent to which the hypothesized model was reproduced with the current data. Overall, it appears that family-related factors are significantly related to problem behaviors. The lack of a significant association between internal locus of control and problem behaviors precluded any possible mediation of negative peer influence on problem behaviors. Nevertheless, no direct relationship between negative peer influence and problem behaviors emerged at the latent variable model estimation phase or the bivariate model phase. Despite the acceptable χ2 /df ratios, CFI, IFI, and RMSEA scores, other findings suggest that the hypothesized model was not adequate and not invariant across gender. Specifically, the substandard GFI scores, significant χ2 values, statistically non38
An interpretation that ILOC mediated the relationship between NPI and Problem Behaviors with the female sample would have been taken with caution. The a priori relationship between NPI and Problem behaviors only was significant for females however, the magnitude was almost non-existent and the direction of the relationship unexpectedly negative (b = .010).
Table 4.13 Results of Latent Variable Model Prior to Tests of Invariance, Across Four Demographic Groups Model
Parameters
χ2
df
χ2 /df
NFI
IFI
CFI
RMSEA
Male
78
382.137***
152
2.514
.899
.936
.935
.051
Female
78
598.945***
152
3.940
.872
.902
.900
.065
Rural
78
516.306***
152
3.397
.866
.901
.900
.065
Urban
78
504.009***
152
3.316
.892
.922
.921
.057
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, * p < .05, ** p < .01, *** p < .001
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145
significant parameters, and differences in statistically significant parameters between groups suggest that the specified model of problem behavior was not entirely supported by the data or invariant across gender. While subsequent invariance analyses were not warranted, their results support the findings from the individual sample analyses. For example, results in Table 4.14 show that the Unconstrained model and subsequent constrained models had adequate χ2 /df ratios, CFI, IFI, and RMSEA scores along with significant χ2 values and inadequate NFI scores. In conjunction with the non-significant parameter estimates, these results show that the data do not support the specified latent variable model.
Figure 4.1 Results for Model of Adolescent Problem Behaviors, Male Sample
146
Adolescent Problem Behaviors
If one were to ignore the above results and assume that the constrained model was true, results also would show that the model was not gender invariant (i.e., the model was different across gender). Table 4.15 shows the change in χ2 and degrees of freedom as parameters in the Unconstrained model are fixed to be equal across gender samples. The last column of Table 4.15 indicates that each additional constraint resulted in a significant difference in model fit. In other words, assuming the unconstrained model is correct, adding constraints to that baseline model resulted in a significantly different (and ill-fitting) model.
Figure 4.2 Results for Model of Adolescent Problem Behaviors, Female Sample
Table 4.14 Results of Latent Variable Model Test of Gender Invariance χ2 /df
NFI
IFI
CFI
RMSEA
980.918***
304 3.227
.884
.917
.916
.042
142
1031.261***
318 3.243
.878
.912
.911
.042
Measurement Intercepts
123
1114.096***
337 3.306
.868
.904
.903
.042
Structural Weights
105
1211.695***
355 3.413
.857
.894
.894
.043
Structural Means
104
1211.993***
356 3.404
.857
.894
.894
.043
Structural Covariances
103
1212.559***
357 3.397
.857
.895
.894
.043
Structural Residuals
97
1281.086***
363 3.529
.849
.887
.886
.044
Measurement Residuals
78
1388.878***
382 3.636
.836
.875
.875
.045
Parameters
χ2
Unconstrained
156
Measurement Weights
Model
df
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, ∆χ2 /∆df = change in χ2 divided by the change in degrees of freedom between nested models. * p < .05, ** p < .01, *** p < .001
148
Adolescent Problem Behaviors
Table 4.15 Results of Nested Latent Variable Model Comparisons from Test of Gender Invariance ∆df Model ∆χ2 Unconstrained ----Measurement Weights 5.343 14 Measurement Intercepts 82.835 19 Structural Weights 97.599 18 Structural Means .298 1 Structural Covariances .566 1 Structural Residuals 68.527 6 Measurement Residuals 107.792 19 ∆χ2 = change in χ2 ∆df = change in degrees of freedom
p --.000 .000 .000 .585 .452 .000 .000
Results for Location The initial model estimates for the location samples were similar to those of the gender samples. Table 4.13 shows that, despite recording adequate χ2 /df , CFI, IFI, and RMSEA scores, the rural and urban samples obtained significant χ2 and a lower than expected GFI score. In addition, not all factor loadings depicted in Figures 4.3 and 4.4 were statistically significant. At the personal level, anger control and internal locus of control were not related to problem behaviors among the urban sample. In contrast, both personal protective factors were related to problem behaviors in the rural sub-sample. At the personal level, rural respondents were similar to female respondents who reported a significant relationship between anger control and problem behaviors. At the interpersonal level, rural respondents were again similar to female respondents. Parental monitoring among rural respondents was directly related to problem behaviors. Furthermore, the relationship between family conflict and problem behaviors was completely mediated by parental monitoring and anger control. Results for urban respondents (Figure 4.4) were almost identical to results for male respondents (Figure 4.1). Parental monitoring was significantly related to problem behaviors and mediated the relationship
Critical Findings
149
between family conflict and problem behaviors. Unlike rural and female respondents, anger control among urban respondents was not related to problem behaviors and did not mediate the relationship between family conflict and problem behaviors.
Figure 4.3 Results for Model of Adolescent Problem Behaviors, Rural Sample
In contrast to the specified model, social bond and parental monitoring were related to negative peer influence, a finding that was similar across all samples. Negative peer influence was significantly related to internal locus of control among urban respondents. However, the relationship was not statistically significant (p < .051) among rural respondents. Social bond was not related to problem behaviors as predicted. Social bond was related to internal locus of control among all samples. An apparent mediated relationship between social bond and problem behaviors appears among rural respondents. However, no
150
Adolescent Problem Behaviors
such relationship was specified and thus no such conclusion can be drawn. At the contextual level, results for the location subgroups differed slightly from the gender subgroups. Among rural respondents, neighborhood risk was significantly related to family conflict and social bond as predicted. Contrary to the specified model (Figure 2.8), neighborhood risk was not related to negative peer influence. Neighborhood risk maintained a significant association with problem behaviors that also was partially mediated via parental monitoring, double-mediated via family conflict and parental monitoring, and double-mediated via family conflict and anger control. Among urban respondents, neighborhood risk was related to all four interpersonal factors. Neighborhood risk retained a significant association with problem behaviors that was partially mediated via parental monitoring, and double-mediated via family conflict and parental monitoring. Altogether, the difference in non-significant factor loadings across samples indicates that the model did not adequately reflect patterns in the data and any patterns that were discerned were non-invariant for gender and location. Given these results, testing for location invariance was unwarranted. Nevertheless, invariance tests supported previous results. For example, results in Table 4.16 show that the Unconstrained model and subsequent constrained models had adequate χ2 /df ratio, CFI, IFI, and RMSEA scores along with significant χ2 values and inadequate NFI scores. When considered in conjunction with the non-significant parameter estimates, these results show that the data do not support the specified latent variable model. Neighborhood risk maintained a significant association with problem behaviors that also was partially mediated via parental monitoring, double-mediated via family conflict and parental monitoring, and double-mediated via family conflict and anger control. Altogether, the difference in non-significant factor loadings across samples indicates that the model did not adequately reflect patterns in the data and any patterns that were discerned were non-invariant for gender and location. Given these results, testing for location invariance was unwarranted. Nevertheless, invariance tests supported previous results. For example, results in Table 4.16 show that the Unconstrained model and subsequent constrained models had adequate χ2 /df ratio, CFI, IFI, and RMSEA scores along with significant χ2
Critical Findings
151
values and inadequate NFI scores. When considered in conjunction with the non-significant parameter estimates, these results show that the data do not support the specified latent variable model.
Figure 4.4 Results for Model of Adolescent Problem Behaviors, Urban Sample
If one were to again disregard the previous findings and assume that the constrained model was true, subsequent results would still reveal that the model was not location invariant. Table 4.17 shows the change in χ2 and degrees of freedom as parameters in the Unconstrained model are fixed to be equal across rural and urban subsamples. The last column of Table 4.17 indicates that each additional constraint resulted in a significant difference in model fit. In other words, the hypothesized model was not location invariant. Overall, several results suggest a possible gender-location interaction resulting from a disproportionate number of urban males and rural females; however, earlier results do not support this
Table 4.16 Results of Latent Variable Model Test of Location Invariance Parameters
χ2
df
χ2 /df
NFI
IFI
CFI RMSEA
Unconstrained
156
1020.611***
304
3.357
.880
.913
.911
.043
Measurement Weights
142
1047.712***
318
3.295
.877
.911
.910
.042
Measurement Intercepts
123
1087.421***
337
3.227
.872
.908
.907
.042
Structural Weights
105
1123.442***
355
3.165
.868
.906
.905
.041
Structural Means
104
1124.267***
356
3.158
.868
.906
.905
.041
Structural Covariances
103
1132.218***
357
3.171
.867
.905
.904
.041
Structural Residuals
97
1201.367***
363
3.310
.859
.897
.896
.042
Measurement Residuals
78
1258.900***
382
3.296
.852
.892
.892
.042
Model
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, ∆χ2 /∆df = change in χ2 divided by the change in degrees of freedom between nested models. * p < .05, ** p < .01, *** p < .001
Critical Findings
153
conclusion. Cross-tabulations of gender and location groupings indicate that males were underrepresented in the urban sample and females were underrepresented in the rural sample though the discrepancy was not statistically significant, χ2 (1) = 3.076, p = .079. To determine an interaction effect, the models would have had to be tested using four-way comparisons. In addition to complicating the analyses, the smaller sample size for each group would effectively reduce the power of the study. Further reduction in the power of this study would have been detrimental since the small effects (or covariances) between several measures may not have been detected. Indeed, the relationships between personal level factors and problem behaviors were very low in magnitude or statistically non-significant. A second area where the model failed to perform as predicted was at the interpersonal level with negative peer influence. The hypothesized relationships between social bond, parental monitoring, and negative peer influence did not manifest in any of the sub-samples. The hypothesized relationship between neighborhood risk and negative peer influence only was significant among urban respondents. No a priori relationship between negative peer influence and problem Table 4.17 Results of Nested Latent Variable Model Comparisons from Test of Location Invariance Model Unconstrained
∆χ2
∆df
p
---
---
---
Measurement weights
27.101 14
0.019
Measurement intercepts
39.709 19
0.004
Structural weights
36.021 18
0.007
Structural means
0.825
1
0.364
Structural covariances
7.951
1
0.005
Structural residuals
69.149
6
0.000
Measurement residuals
57.533 19
0.000
∆χ2 = change in χ2 ∆df = change in degrees of freedom
154
Adolescent Problem Behaviors
behaviors was ever established, however. Thus, no indirect effects through internal locus of control could be established. Another measure of model performance was the amount of variance in a measure that was accounted for by the model (i.e., the variables pointing to that measure). Table 4.18 delineates the amount of variance in each measure in the model that is accounted for by it associated predictors. Negative peer influence had the lowest amount of explained variance across all four subgroups. Family conflict and social bond also had the least amount of variance explained. Nevertheless, the amount of variance explained in problem behaviors was acceptable for an ill-fitting model. The proportion of problem behavior variance explained among males equaled .397, followed by .521 among females, .566 among rural respondents, and .404 among urban respondents. Exploratory Modeling Analyses To the extent that Figures 4.1 through 4.4 differed from the hypothesized model, they also provided insight into the mechanisms of problem behaviors that operate for each respective sample. That is, Figure 4.1 through 4.4 are not only the results of estimating the model specified in Figure 2.8, they also serve as newly specified models that correspond uniquely to each sample. To determine the modified model underlying the initial results in Figures 4.1 through 4.4, the model for each sample was specified using the following criteria: a) the model should exclude any statistically insignificant pathway and b) any pathway that did not connect (i.e., “dead-ended”) with the outcome variable (problem behaviors) should be excluded. Figure 4.5 and Figure 4.6 display the newly specified gender models while Figure 4.7 and Figure 4.8 show the new location models. The results for these estimated models are listed in Table 4.19. The fit indices for the male sub-sample indicate excellent model fit: nonsignificant χ2, χ2 /df ratio approximating one, relative fit indices above .9, and RMSEA below .018. The proportion of problem behavior variance explained among males equaled .388, followed by .522 among females, .549 among rural respondents, and .378 among urban respondents. The female and urban
Critical Findings
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Table 4.18 Percentage of Variance in Each Indicator Explained by Its Respective Factor, for Gender and Location Groups Male
Female
Rural
Urban
Anger Control
0.15
0.216
0.202
0.15
Indicator 1
0.495
0.525
0.506
0.51
Indicator 2
0.698
0.593
0.675
0.623
Indicator 3
0.696
0.663
0.67
0.686
Family Conflict
0.026
0.069
0.074
0.031
Indicator 4
0.551
0.552
0.551
0.54
Indicator 5
0.65
0.613
0.659
0.619
Indicator 6
0.535
0.492
0.454
0.56
Parental Monitoring
0.174
0.281
0.171
0.247
0.388
0.391
0.456
0.341
0.628
0.632
0.705
0.592
0.496
0.546
0.475
0.563
0.101
0.173
0.134
0.141
0.379
0.396
0.423
0.361
0.396
0.362
0.368
0.395
0.387
0.356
0.421
0.336
0.06
0.067
0.074
0.058
0.668
0.695
0.715
0.658
0.538
0.591
0.582
0.564
0.486
0.509 0.46 0.541 (continued on the next page)
Indicator 7 Indicator 8 Indicator 9 Int. Locus of Control Indicator 10 Indicator 11 Indicator 12 Social Bond Indicator 13 Indicator 14 Indicator 15
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Table 4.18 Percentage of Variance in Each Indicator Explained by Its Respective Factor, for Gender and Location Groups (continued) Male
Female
Rural
Urban
0.397
0.521
0.566
0.404
0.775
0.597
0.614
0.735
0.604
0.42
0.384
0.604
0.482
0.671
0.537
0.571
Neg. Peer Influence
0.002
0.002
0.015
0.016
Neighborhood Risk
n.a.
n.a.
n.a.
n.a.
Problem Behaviors Indicator 16 Indicator 17 Indicator 18
n.a. – not applicable, item is not a predicted variable
sub-samples had a statistically significant χ2 value; however, all other fit indices were adequate. The rural sub-sample had the least acceptable model fit including a significant χ2 and a low NFI value. The high RMSEA score for the rural sub-sample is a result of the extra parameters in the model. These extra parameters also account for the additional problem behavior variance explained in the model. Overall, the modified models suggest that family factors play a critical role in explaining problem behaviors. The strength of the association between family factors and problem behaviors is persistent across gender and location. The absolute difference in problem behavior variance explained by the initial and modified models was minimal: .009 for males, .001 for females, .017 rural respondents, and .026 for urban respondents. Given a choice between the initial and modified model, the parsimony rule would require the selection of the simpler modified model. The implications of the results for the initial and modified models are discussed in the next chapter.
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Figure 4.5 Results for Exploratory (Modified) Model of Adolescent Problem Behaviors, Male Sample
Figure 4.6 Results for Exploratory (Modified) Model of Adolescent Problem Behaviors, Female Sample
Table 4.19 Results of Modified Latent Variable Model, Across Four Demographic Groups Model
Parameters
χ2
df
χ2 /df
NFI
IFI
CFI
RMSEA
Male
34
36.679 n.s.
31
1.183
.980
.997
.997
.018
Female
45
148.264***
59
2.513
.953
.971
.971
.047
Rural
65
498.487***
144
3.462
.869
.903
.902
.066
Urban
33
69.066***
32
2.158
.970
.984
.984
.040
NFI = normed fit index, IFI = incremental fit index, CFI = comparative fit index, RMSEA = root mean square error of approximation, * p < .05, ** p < .01, *** p < .001, n.s. = not statistically significant.
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Figure 4.7 Results for Exploratory (Modified) Model of Adolescent Problem Behaviors, Rural Sample
Figure 4.8 Results for Exploratory (Modified) Model of Adolescent Problem Behaviors, Urban Sample
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CHAPTER 5
Discussion and Implications
RESEARCH QUESTION This study was conducted to answer the following research question: Is the etiology of adolescent problem behaviors adequately described by an ecological model comprised of personal, interpersonal, and contextual factors? To address this question, an explanatory model of problem behaviors that included personal, interpersonal, and contextual risk and protective factors was developed using an adapted social systems framework (see Figure 2.7). The personal, interpersonal, and contextual factors in the model were mapped onto the infra-, micro/meso-, and exo-systems of the adapted framework, respectively. The adapted framework could accommodate the study of macro-, exo-, meso-, micro-, and infra-system influences on the behavioral system. This current study (i.e., model), however, only examines how influences operate between the exo- and infra-systems to explain problem behaviors within the behavior-system. The factors used in this model included: a) a contextual risk factor, perceived neighborhood risk, b) two interpersonal risk factors, family conflict and negative peer influence, c) two interpersonal protective factors, parental monitoring and social bond, and d) two personal protective factors, anger control and internal locus of control. Results indicated that the hypothesized relationships in the model were significantly different from the patterns found in the data of each sample. The hypothesized models did not meet all of the four criteria postulated for non-rejection. Although unwarranted, subsequent tests of invariance showed that the models differed across gender or location. 161
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Nevertheless, several portions of the hypothesized model were statistically significant and supported some of the hypothesized relationships described in Chapter 2. The results of these initial models led to hypothesizing alternate or modified models. These modified models were based solely on the statistically significant pathways that had some viable explanatory value for problem behaviors (i.e., could still be linked directly, or indirectly, to problem behaviors). The modified models were slightly different for each sample but each shared more than half of the factors found in the original model, including: perceived neighborhood risk, family conflict, parental monitoring, anger control, and problem behaviors. The modified model results revealed that a portion (i.e., top half) of the original theoretical model was supported by the data. Specifically, results showed that the modified model adequately described the data for the sample of male adolescents as indicated by a non-significant χ2 (see Table 4.18). The χ2 values for the female, rural, and urban samples were statistically significant though the χ2 /df ratio was well within the specified range of five. To the extent that differences between the specified model and data are due to sensitivities from sample size, the low χ2 /df ratio suggests that the models may adequately explain problem behaviors among female, rural, and urban adolescents. The significant relationships between the personal, interpersonal, and contextual factors in these modified models also provide limited support for the premise that a multi-level model can adequately describe problem behaviors. These results suggest that the influence of external (perceived) risk factors on problem behaviors can be partially mediated by interpersonal risk and protective factors (i.e., family conflict and parental monitoring, respectively). In addition, the influence of interpersonal factors among female and rural youth is further mediated by personal level factors (i.e., anger control). The factors in the modified models accounted for over half of the variance in problem behaviors among the female and rural respondents and over 37% of the variance among the male and urban respondents. Overall, the partial results from the original model and the results from the modified models provide some answers to the general research question in support of multi-level models of problem behaviors. The following section discusses the results of the hypothesized mediated paths (i.e., indirect effects) that illustrate the process through which
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different ecological levels combine to explain problem behaviors. Implications for gender and location differences also are discussed. Hypothesized Mediated Relationships across Conceptual Levels Paths 'A' 'B' 'C': (NEIGHBORHOOD RISK to FAMILY CONFLICT to ANGER CONTROL to PROBLEM BEHAVIORS). It was predicted that Paths 'A', 'B', and 'C' in Figure 2.9a would be statistically significant. Specifically, it was hypothesized that the relationship between neighborhood risk and problem behaviors was mediated by family conflict which, in turn, was mediated by anger control. The results revealed that among males, females, and urban respondents, the relationship between neighborhood risk and problem behaviors could only be partially mediated by family conflict and parental monitoring. The partial (as opposed to full) mediation of perceived neighborhood risk and problem behaviors was the result of a statistically significant39 relationship (Path 4) between these two constructs in both the bivariate models and the latent variable models. The double-mediated relation between neighborhood risk and problem behaviors depicted by Paths 'A', 'B', and 'C' was not tenable among any of the four demographic samples, for two reasons. The first reason applies to male and urban samples while the second reason applies to all samples. First, Path C only was statistically significant among female and rural respondents but not male and urban respondents. A non-statistically significant Path C among male and urban respondents precluded the possibility that anger control could mediate the relationship between family conflict and problem behaviors. Second, the a priori bivariate relationship between neighborhood risk and anger control (Path 1) was not established among any of the four samples. The lack of support for Path 1 precluded any potential mediation via Path A or Path B. Overall, the hypothesized pathway between neighborhood risk and problem behaviors (Paths 'A', 'B', and 'C) was not supported. Despite the lack of support for this double-mediated pathway, the significant association between perceived neighborhood risk and problem behaviors suggests 39
The pathway had to be statistically significant and the model had to have adequate fit indices.
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that the perceived environment is a strong determinant of behavior in the adapted framework and model (Bronfenbrenner, 1979; Jessor & Jessor, 1977; Williams, Singh, & Singh, 1994) The lack of support for a double-mediated pathway between neighborhood risk and problem behaviors, via Paths 'A', 'B', and 'C, has additional implications in relation the overall research question and the adapted social systems framework. First, the influence of the contextual factor did not extend across all of the predicted ecological levels. In terms of systems theory, the influence of the exo-system on the behavior-system did not extend across the meso/micro-system or the infra-system. Recall that the adapted framework can accommodate the study of macro-, exo-, meso-, micro-, and infra-system influences on the behavioral system. The current study (i.e., model), however, only examined how influences operate between the exo- and infrasystems to explain problem behaviors within the behavior-system. The lack of mediation between neighborhood risk, an exo-system factor, and anger control, an infra-system factor, reduces the scope of the model. That is, the remaining mediated pathways between family conflict and problem behavior (Path B and Path C) limit the scope of the model to a study of influence between micro- and infra-systems on the behavior-system. Path C was not supported among male and urban respondents, and precluded any further analysis of influences across ecological levels. Among female and rural respondents, results showing that anger control mediated the influence of family conflict on problem behaviors provide limited support for the premise that influences extend across ecological levels. The significant Path C among female and rural respondents also lend limited support to the premise that personal factors mediate the influence of more distal (interpersonal) influences on behavior. These results also have implications regarding the adequacy of etiological models of problem behaviors to account for problem behavior among females. Proponents of a feminist based theory of female delinquency argue that most delinquency theories do not adequately explain female problem behaviors (Chesney-Lind, 1989; Klein, 1973). For example, Chesney-Lind (1989) has suggested that most problem behaviors are trivial, generally amount to status offenses, and that this pattern is more indicative of female problem behaviors (Chesney-Lind, 1989; Klein, 1973). Consistent with this suggestion,
Discussion and Implications
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the current study incorporates two measures relevant to status offending among females: running away and alcohol use. The amount of variance in the problem behavior construct that is explained by the model was larger for females (52%) than males (40%). Not withstanding the model fit indices that led to the model rejections, the difference in variance explained suggests that the hypothesized model more adequately explains problem behaviors for females compared to males. It may be argued that the additional explained variance among female problem behaviors might derive from orphaned pathways that do not directly explain problem behaviors. The statistically significant relationship between anger control and problem behaviors (Path C) however, does not support this argument. An underlying premise of the adapted framework is that personal level factors (i.e., factors that fall within an infra-system), mediate the influence of other, more distal factors. Anger control among females directly contributed to the variance explained in problem behaviors. Anger control also performed as predicted and mediated the influence of family conflict on female problem behaviors. The absence of any association between anger control and problem behaviors among male and urban respondents was unexpected given previous studies linking anger control to aggression, a component of problem behaviors in this study (Coles, Greene, & Braithwaite, 2002; Griffin, Scheier, Botvin, Diaz, & Miller, 1999). Previous research also has shown a gender-by-self-control interaction whereby males who scored low on self-control reported more property related delinquency (Pfefferbaum & Wood, 1994); another component of problem behaviors in this study. Thus, one would have expected that a relationship between anger control and problem behaviors that is moderated by gender would favor a stronger association among males than females. Descriptive results revealed higher mean scores for all problem behavior indicators for males, compared to females. This finding is consistent with literature and prevalence data showing higher prevalence rates among males. Research has revealed anger control to mediate the relationship between parental monitoring and problem behaviors (Gibbs, Giever, & Martin, 1998). Such a path was not postulated for the current model since the wording (i.e., face validity) of underlying indicators would not have not allowed a logical interpretation of any potential relationship. Future test of the current model could include a pathway
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between parental monitoring and anger control to assess whether anger control mediates parental monitoring and problem behaviors. Subsequent tests of the current model also could a) continue to test for gender differences, b) include status offenses, and c) consider using a second order construct40 of problem behaviors. Using a second order construct would allow one to examine the relationship between risk and protective factors and a second order construct of problem behaviors (Farrell et al., 2000). In addition, one could examine whether gender moderates the relationship between explanatory factors and the subconstructs of problem behavior (i.e., delinquency, aggression, drug use). Finally, one also could include a macro-level construct of paternalism. Chesney-Lind (1989) argues for such a measure in order to determine a) how distal factors influences perceptions of gender roles and b) what impact perceptions of gender roles has on females’ reactions (e.g., running away) to stress (e.g., sexual abuse). Paths 'A' 'D' 'E': (NEIGHBORHOOD RISK to FAMILY CONFLICT to PARENTAL MONITORING to PROBLEM BEHAVIORS). It was predicted that Paths 'A', 'D', and 'E' in Figure 2.9b would be statistically significant. Specifically, it was hypothesized that the relationship between neighborhood risk and problem behaviors was mediated by family conflict which, in turn, was mediated by parental monitoring. Parental monitoring, in turn, is negatively related to problem behaviors. Adolescents who witness many community crimes will engage in more conflicted family interactions, resulting in less parental monitoring, leading to increased problem behaviors. As noted in the previous section, results revealed that the relationship between neighborhood risk and problem behaviors could only be partially mediated by family conflict and parental monitoring, among males, females, and urban respondents. The combination of statistically significant bivariate pathways (Path 3 and Path2) and statistically 40
A second order construct is a measurement model containing two or more measurement models whose constructs load on to another construct. In the case of problem behaviors, delinquency, aggression, and drug use would be separate measurement models with their own indicators. The construct in each of these measurement models (i.e., delinquency, aggression, drug use) would serve as the indicators for the second order factor, problem behaviors.
Discussion and Implications
167
significant hypothesized latent model pathways (Paths ‘A’ ‘D’ ‘E’) revealed that family conflict fully mediated the relationship between neighborhood risk and parental monitoring. Overall, the hypothesized pathway between neighborhood risk and problem behaviors (Paths 'A', 'D', and 'E’) was supported. At a broad level, results among all respondents are consistent with Bronfenbrenner’s (1979) and Jessor & Jessor’s (1977) contention that the perceived environment is a critical determinant of behavior. Perceived neighborhood risk directly accounted for seven to nine percent of the variance is problem behaviors. Parental monitoring is another construct that is based on the perceived environment. In this study, respondents indicated how aware they believed their parents were about their activities and whereabouts. Parental monitoring accounted for 17% to 24% of the variance in problem behaviors across the four groups. These results (for Paths ‘A’ ‘D’ ‘E’) have implications for social psychological measures related to problem behaviors. Parental monitoring in this study was measured using items that captured adolescents’ perceptions of parental involvement and parental knowledge of their whereabouts. This operationalization of parental monitoring is in accordance with the very definition of the study of social psychological phenomena involving the “actual, imagined, or implied presence of others (Allport, 1985).” Parental monitoring, operationalized as a social psychological factor in this study, was significantly associated with problem behaviors. Respondents who had strong perceptions that their parents were monitoring them reported fewer problem behaviors. In contrast, support for the mediating effects of personal level factors such as anger control and internal locus of control (discussed below) was limited or non-existent. Altogether, these results suggest that social psychological factors (e.g., the perceived monitoring) are a strong determinant of adolescent problem behavior. From a system theory perspective, Paths ‘A’ ‘D’ ‘E’ also illustrate how microsystems have a strong influence on behavior. Specifically, these paths demonstrate how a meso-system (i.e., the interaction between two microsystems) of family conflict and parental monitoring can mediate the influence of an exo-system influence (i.e., neighborhood risk) on the behavior-system (i.e., problem behaviors). From the perspective of model testing, this pathway supports the notion
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that interpersonal risk factors mediate the influence of contextual factors on problem behavior. Paths 'A', 'D', and 'E’ do not include a personal factor so support the overall research question is limited. From a prevention perspective, family conflict and parental monitoring further emphasize the role of the family in reducing adolescent problem behavior. This finding also is in line with research showing that despite the increased relevance of peers during adolescence, parental influence continues to be important (Simons, Chao, Conger, & Elder, 2001). Findings regarding parental influences also are supported by research (Jacobson & Crockett, 2000; Steinberg, 1986; Weintraub & Gold, 1991) demonstrating an inverse relationship between parental monitoring and problem behaviors. Gender has been found to moderate this relationship, although results are mixed. Weintraub and Gold (1991) reported a stronger association between parental monitoring and problem behaviors among boys while Galambos and Maggs (1991) found a stronger association among girls. Jacobson and Crockett (2000) and Steinberg (1986) have reported a gender/age interaction such that monitoring was more effective among older boys and younger girls. Jacobson and Crockett (2000) suggest parental monitoring is differentially relevant to boys and girls given that each mature at different times during adolescence. Girls may interact with older peers at a younger age (fifth through eighth grade) placing them at earlier risk while boys may associate with older, riskier peers at a later age (eighth through twelfth grade). While age comparison could not be made with the current sample of eighth grade students, results on gender differences suggest that the negative relationship between parental monitoring and problem behaviors was stronger among males (b = -.236) than females (b = -.172). Earlier (descriptive) results however, revealed higher levels of parental monitoring among female respondents. The combination of these results suggest that while parental monitoring may be higher among females, it’s relationship to problem behaviors may be more important for boys, who engage in more problem behaviors. Existing research on rural/urban differences indicate that problem behaviors are more prevalent in urban areas (Farrell et al., 2000; Gottfredson et al., 1996). Early descriptive results support this research and reveal higher levels of reported problem behaviors among urban adolescents.
Discussion and Implications
169
Parental monitoring was lower among urban youth, compared to their rural counterparts. The negative relationship between parental monitoring and problem behaviors, however, was stronger among urban adolescents (b = -.233) compared to rural adolescents (b = -.177). The implications for location are similar to those for gender; while parental monitoring may be higher among rural adolescents, its relationship to problem behaviors may be more important for urban adolescents, who engage in more problem behaviors. Paths 'F' 'M': (NEIGHBORHOOD RISK to NEGATIVE PEER INFLUENCE to PROBLEM BEHAVIORS). It was predicted that Paths 'F' 'M' in Figure 2.9c would be statistically significant. Specifically, it was hypothesized that the relationship between neighborhood risk and problem behaviors was mediated by negative peer influence. Adolescents who witness crimes in their neighborhood will conform to antisocial peer demands and engage in more problem behaviors. Despite a significant relationship between neighborhood risk and problem behaviors (Path 4) across male, female, and urban groups, results revealed that neighborhood risk was associated with negative peer influence only among urban respondents. The relationship among this group however, was negative and not in the predicted direction. In addition, urban respondents reported less negative peer influence, more perceived neighborhood risk, and more problem behaviors, than rural respondents. Negative peer influence was not related to problem behaviors across any of the groups. These combined results suggest that negative peer influence occurred less frequently among urban youth; but those youth who reported acceding to peer influence were (perhaps41) less likely to have witnessed crimes in their neighborhood. Overall, the hypothesized pathway between neighborhood risk and problem behaviors (Paths 'F', and 'M') was not supported. The lack of association between negative peer influence and problem behaviors was unexpected given the literature showing a positive association between these constructs (Farrell et al., 2000; Flannery, Williams, & Vazsonyi, 1999; Pleydon & Schner, 2001; 41
Statistically significant differences were not calculated and preclude more definitive interpretations.
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Shoemaker, 2000; Simons, Chao, Conger, & Elder, 2001). The current findings may have resulted42 from the use of a single indicator. As noted in Appendix F, measuring a construct with multiple indicators is analogous to viewing an object from several vantages points. The reduction of the negative peer influence measure from a three-item construct to a single indicator variable very likely eliminated potential dimensionalities or facets of the negative peer influence construct.43 Perhaps the different facets of the negative peer influence construct that were not tapped into by the single indicator were critical for this indicator to correlate with the problem behavior construct and other discriminant factors. Future tests of the model could include more effective measures of negative peer influence, preferably standardized scales with reliabilities above .70 (Arthur, Hawkins, Pollard, Catalano, & Baglioni, 2002). Paths 'F' 'H' 'I': (NEIGHBORHOOD RISK to NEGATIVE PEER INFLUENCE to INTERNAL LOCUS OF CONTROL to PROBLEM BEHAVIORS). It was predicted that Paths 'F' 'H' 'I' in Figure 2.9d would be statistically significant. Specifically, it was hypothesized that the relationship between neighborhood risk and problem behaviors was mediated by negative peer influence, which, in turn, was mediated by internal locus of control. Adolescents who witness community crimes will conform to antisocial peer demands, leading them to perceive their locus of control as external, resulting in greater problem behaviors. The predicted double-mediated path did not materialize for three reasons. First, the relationships discussed in the previous section between neighborhood risk, negative peer influence, and problem behaviors were not statistically significant. Second, the bivariate relationship between neighborhood risk and internal locus of control was not established among the female and urban samples; precluding any mediation within these two samples. Third, the relationship 42
An additional interpretation for the lack of a non-significant relationship is that the negative peer influence measure was not “negative enough.” While the item may have captured respondents’ susceptibility to acquiesce to peer demands, it remains unclear whether those demands were negative. 43 A construct would have been used with two or more indicators.
Discussion and Implications
171
between internal locus of control and problem behaviors was not statistically significant for male, female, and urban respondents. The lack of association between internal locus of control and problem behaviors (among three of the four groups) and the results for anger control (among male and urban respondents) suggest that the personal level factors have little or limited bearing on problem behaviors among these samples. Despite the lack of association between internal locus of control and problem behaviors, the former was significantly (and negatively) associated with negative peer influence for the male, female, and urban groups44. These results demonstrate convergent validity between negative peer influence and internal locus of control: Both measures appear to tap into adolescents’ susceptibility to acquiesce. Paths 'J' 'L': (NEIGHBORHOOD RISK to SOCIAL BOND to PROBLEM BEHAVIORS). It was predicted that Paths 'J' 'L' in Figure 2.9e would be statistically significant. Specifically, it was hypothesized that the relationship between neighborhood risk and problem behaviors was mediated by social bond. Adolescents who witness more community crime will experience a weak social bond resulting in greater problem behaviors. The previously established bivariate relationship between neighborhood risk and problem behaviors among male, female, and urban samples allowed for the possibility that social bond mediated that relationship for those three groups. Neighborhood Risk was significantly related to social bond across all samples. The relationship between social bond and problem behaviors, however, was not statistically significant for any of the samples. The lack of association between social bond and problem behaviors is inconsistent with Hirschi’s (1969) social control theory and subsequent studies (Cernkovich, & Giordano, 1992; Zingraff et al., 1994) showing a link between social bond and problem behaviors. This lack of association between social bond and problem behaviors may be a result of the manner in which social bond was measured. The current measure of social bond only assessed the first 44
Path H among rural respondents was b = -.053, and approached statistical significance p = .051.
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component of Hirschi’s definition of social bond which includes: attachment to conventional others, commitment to conventional activities, involvement, and beliefs. It may be that problem behavior is associated with the other components of social bond that were not captured in this study. For example, a study by Jenkins (1997) demonstrated that different components of social bond may be related to different components of problem behaviors. Jenkins found that the attachment and involvement components of social bond did not predict school related crime (e.g., drug use, weapons carrying, theft, and property damage). All components of social bond however, predicted school misconduct (e.g., cheating, talking in class, defacing classroom property). These findings suggest that components of social bond may be predictive of milder forms of problem behaviors but not more serious adolescent crime. Although the problem behavior measure in this study included milder forms of problem behaviors, these behaviors may be related to the other components of social bond (i.e., commitment to conventional activities, involvement, and beliefs) more so than a simple attachment to others. To address this issue, future tests of the current model should test the four components of social bond. Paths 'G' 'H' 'I': (PARENTAL MONITORING to NEGATIVE PEER INFLUENCE to INTERNAL LOCUS OF CONTROL to PROBLEM BEHAVIORS). It was predicted that Paths 'G' 'H' 'I' in Figure 2.9f would be statistically significant. Specifically, it was hypothesized that the relationship between parental monitoring and problem behaviors was (partially45) mediated by negative peer influence, which, in turn, was mediated by locus of control. Adolescents who believe they are monitored will conform less to negative peer influence, leading them to view their locus of control as internal, resulting in less problem behaviors. Two thirds of this double-mediated pathway (Paths ‘H’ ‘I’) is similar to the Neighborhood Risk, Negative Peer Influence, Internal Locus of Control, and Problem Behaviors pathway (Paths 'F' 'H' 'I'), discussed above. The results and implications for this portion of the pathway also are similar and are not repeated here.
45
Partially mediated given the hypothesized Path E.
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173
Although the lack of association between internal locus of control and problem behaviors is not consistent with past studies (Downs & Rose, 1991; Peiser & Heaven, 1996) this literature does provide some direction for future testing. For example, studies have shown that locus of control mediates the relationship between parenting effectiveness and problem behaviors. In the current study, parental monitoring is indirectly related to internal locus of control via negative peer influence. Contrary to predictions, however, the link between parental monitoring and negative peer influence was not significant across all samples. These results are not supported by previous studies showing an increased association with negative peers and greater susceptibility to peer influence among unsupervised adolescents (Flannery et al., 1999; Simons et al., 2001). Earlier studies (Downs et al., 1991; Peiser et al., 1996) and current findings suggest that future models could be respecified with a hypothesized pathway from parental monitoring to internal locus of control (Path 6). Indeed, a priori bivariate models of parental monitoring and internal locus of control in this study were adequate and had statistically significant pathways (i.e., Path 6) between the two constructs. Path 6 remained statistically significant (b = .086, p < .05) in the subsequent test of the model within the female sample. Descriptive results also showed that females reported greater means scores on parental monitoring and lower means scores (i.e., more external) on locus of control. Altogether, these findings suggest that the protective benefit of parental monitoring comes at the cost of a weakened (i.e., externalized) locus of control among females. This potential implication could be explored in future studies. Such a study could examine the moderating effects of gender and locus of control to determine whether models are predictive of problem behaviors across four groups: females with an internal locus of control, females with an external locus of control, males with an internal locus of control, and males with an external locus of control. Paths 'K' 'H' 'I': SOCIAL BOND to NEGATIVE PEER INFLUENCE to INTERNAL LOCUS OF CONTROL to PROBLEM BEHAVIORS). It was predicted that Paths 'K' 'H' 'I' in Figure 2.9f would be statistically significant. Specifically, it was hypothesized that the relationship between parental monitoring and problem behaviors was mediated by
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negative peer influence which, in turn, was mediated by locus of control. Adolescents with a strong social bond will conform less to negative peer influence, leading them to view their locus of control as internal, resulting in fewer problem behaviors. The hypothesized mediated relationship between of negative peer influence and problem behaviors (Paths ‘H’ ‘I’) has been addressed in the sections pertaining to Pathways 'F' 'H' 'I' and 'G' 'H' 'I', above. The results and implications for Paths ‘H’ and ‘I’ also are similar and are not repeated here. Social bond was significantly and positively related to internal locus of control as indicated by the bivariate models for all samples. This relationship allowed subsequent mediation analyses in the latent variable model. Several results regarding social bond were unexpected or unpredicted. First, the significant relationship between social bond and internal locus of control in the bivariate models remained significant during the test of the latent variable model. Hence, any indirect relationship between social bond and internal locus of control are partially mediated. Second, the results regarding the relation between social bond and negative peer influence were similar to those of parental monitoring, neighborhood risk, and negative peer influence. Contrary to predictions, the link between social bond and negative peer influence was not significant across all samples. This result is inconsistent with research showing a relationship between social bond and negative peer influence whereby the latter mediates the inverse relation between social bond on problem behavior (Erickson & Crosnoe, 2000). In a study of 4,625 ninth through twelfth graders from California and Wisconsin, Erickson and Crosnoe found that negative peer associations and susceptibility to negative peer influence mediated the influence of social bond on delinquency (e.g., weapons carrying, fighting, theft) and substance use (e.g. cigarettes, alcohol, and marijuana). The effect of social bond on susceptibility and peer associations was similar for boys and girls; though the effect of peer influence on delinquency was greater for boys. For substance use, the negative impact of susceptibility was stronger among females while peer associations had a stronger association for males. In conjunction with previous results regarding social bond (Paths ‘J’ ‘L’), the lack of association between social bond and negative peer influence also suggests that peer influence may be related to other components of social bond that were not measured in this study.
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Limitations The following section discusses several limitations that warrant consideration. These limitations are divided into three sections addressing issues related to data, measurement, and the generalizability of results. This section is followed by a discussion of the implications of the current findings for future research and problem behavior prevention efforts. Data Cross-sectional data and causality. The cross-sectional data used in this study do not allow causal inferences to be made of the findings discussed in the previous section. Assumptions regarding the direction of effects in the structural model were made in accordance with past theory and research. In the absence of temporally-separate measurements however, model results only represent covariations among the variables under study. An important implication for future research will be to collect longitudinal data to determine whether current hypothesized factors of problem behaviors (at say, Time1) are associated with problem behaviors or other factors, at a later time (e.g., Time2). For example, the hypothesized relationship between parental monitoring and problem behaviors may be mediated by negative peer influence. The relationship between parental monitoring and negative peer influence may require some minimum period to adequately manifest. This argument is supported by recent work by Simons et al. (2001) showing that effective childhood parenting impacts problem behaviors by determining subsequent adolescent peer associations. Future longitudinal tests of the model should include adequately spaced assessment waves. Although this study relied on cross-sectional data, the logic within a theoretical framework helped in determining directional effects. For example, personality variables within the infrasystem were hypothesized to mediate the effects of micro-system (interpersonal) factors on problem behaviors. External forces that come to bear on the individual in favor of engaging in problem behaviors are thus filtered (i.e., mediated) by personal characteristics within the individual. Results among female and rural respondents, showing that anger control mediates the relationship between family conflict and problem
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behaviors, lend partial support to the directional premise proposed in this framework. Missing data. Missing data occurred as a result of three conditions: Absenteeism, active consent, and item non-response. Respondents who did not attend school the day of data collection were not included in this study. Although a percentage of students are absent each day, a portion of those students may represent adolescents who may be truants on the verge of completely dropping out. There are competing arguments regarding the impact dropping out may have on participation in problem behaviors46. On the one hand, social control theory (Hirschi, 1969) would suggest that adolescents who drop out of school have severed a social bond and may feel freer to engage in problem behaviors. On the other hand, strain theory (Cloward & Ohlin, 1955; Cohen, 1965; Merton, 1957) would suggest that school is source of social pressure; a place where socially acceptable goals (e.g., success, wealth) are instilled among students who do not have access to resources (e.g., opportunity) to ultimately attain those goals. Once removed from this social strain, a school dropout may be less inclined to engage in problem behaviors. Despite evidence in favor of both perspectives (Drapela, 2005), it remains that a percentage of potential respondents in the current study who may have been at greater risk to engage in problem behaviors were not included in this sample. As such, present results may underrepresent the prevalence and association between several factors that explain problem behaviors. Another source of missing data resulted from the use of active consent procedures. As noted earlier, school sites that required passive consent procedures had greater than 90% response rates while school sites that required active consent procedures had approximately 33% response rates. Active consent procedures required that students take home a consent form to their parents and return the signed consent form to their teachers prior to completing a questionnaire. It is likely that students most at risk for problem behaviors would not have provided their parents with a consent form or turned in their consent forms. Furthermore, at-risk students, who are not monitored by their 46
This argument excludes the fact that truancy is a form of problem behaviors. In some jurisdictions, truancy is limited to those under 16 years of age. Assuming an average age of 18 for a high school graduate, there are two years during which an adolescent may lawfully decide to leave school.
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parents, would have been less likely to obtain a signed consent form from their parents. The implications are mitigated however, by mean comparisons that revealed no nominal differences in problem behaviors among active and passive consent groups. The third source of missing data results from students not responding to particular items in the questionnaire. For example, data may be systematically missing if items tap into sensitive issues (e.g., income, religion, crime) or are at the end of the questionnaire and respondents fatigue or run out of time. Systematic loss of data may raise concerns about the validity of the survey instrument or reveal characteristics about respondents (e.g., reading proficiency to complete questionnaire); a factor that may be relevant to the outcome measure. In this study, missing data were less than five percent for each variable and Little’s (1998) test for MCAR did not reject the hypothesis that data are missing completely at random. Missing data analysis revealed little or no differences in the values of means and standard deviations obtained from listwise, pairwise, EM, and regression estimations of missing data. The underlying distribution of the missing cases and its potential impact on the available data however, remain undeterminable. Missing data also required the use of FIML to estimate the structural models. While FIML is less biased than listwise or pairwise procedures, it is not without bias and that bias is unmeasured. Missing data also precluded the use of the Lagrange Multiplier (LM) as a means determining which pathways to remove to improve fit of the modified exploratory models. Relying on significance levels and “dead end” pathways was a statistically and theoretically defensible strategy for exploring models suggested by the data. Nevertheless, the additional statistical information provided by the LM would have been useful in determining other empirically relevant associations that may have been omitted from the original latent variable model. While the modified models adequately reflect patterns in the data, alternate models may adequately represent the data as well. Non-normal data. Non-normal data can be a concern when, for example, variables which are non-normally distributed in opposite directions can sometimes suggest strong negative correlations, when no such correlations exist. Data screening efforts were undertaken to assess the presence and magnitude of non-normal data. Although nonnormally distributed data were detected, no transformed data were used in this study for several reasons. First, the nature of the project
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involved the study of behaviors that one expects not to be normally distributed (Tabachnick & Fidell, 1996). Second, transformation of non-normal variables did little to improve distributions and sometimes worsened kurtosis (see Appendix F). Third, the loss of interpretability resulting from transformed data outweighed any gains from the slight distributional improvement. Instead, greater reliance was given to the ability of FIML estimation to be robust against deviations from normality. Measurement Single-Item Measures and Directional Ambiguity. Negative peer influence is a critical explanatory factor of problem behaviors. Two of the three available indicators for the negative peer influence construct, however, were not sufficiently adequate for analysis, resulting in the use of a single item measure. Using a single item measure essentially reduced the number of views or perspectives about this construct. Using a single item measure as a proxy for negative peer influence may have limited the potential association of negative peer influence to other factors. Negative peer influence failed to associate with any risk or protective factors except internal locus of control. It is presumed that the relation between negative peer influence and internal locus of control was more of a reflection of respondents’ susceptibility to acquiesce to peers and the belief that what happens to them depends on others. The underperformance of the negative peer influence construct disrupted a large number of the hypothesized mediated pathways. Present results make it unclear whether a stronger (i.e., directionally clear) set of indicators for negative peer influence would have supported the hypothesized pathways and improved the overall fit of the model. Method Bias. Another limitation is that the independent and dependent variables in this study were measured via self-reports, and thus share common method variance. The use of self-report methods was an appropriate means of accessing respondents’ perceived environment. The perceived environment is a key component of Jessor & Jessor’s (1977) problem behavior theory and Bronfenbrenner’s (1979) system’s theory, from which the framework used in this study is derived. Nevertheless, when a single data collection method is used, a portion of the shared variance among measures may not be a result of a
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common underlying factor or relationship. A portion of the shared variance may instead reflect a propensity to respond (i.e., self-report) to survey questions in a particular manner. Future tests of this model should incorporate multi-method designs. For example, video data of family interactions (e.g., dinner table discussions) could be coded to determine the frequency and quality of parental monitoring. These data can be combined with measures accessing respondents perceived parent monitoring, to obtain a more diverse measure of parental monitoring. Relying solely on selfreports, also leaves unanswered the question of social desirability. Although participants were insured that their responses would remain completely anonymous, it is possible that participants answered questions in a socially desirable manner; perhaps under-reporting their extent of engagement in problem behaviors. Secondary data and Construct validity. The data and measures for this study derive from a separate project and were not collected specifically for this study. Much attention was given to matching measures that tap into related theoretical constructs with the measures available in the data set. Reliabilities and factors analyses were conducted to assess the internal consistency of the multi-item measures. While the constructs were tested for reliability, they remain proxies for the constructs of interest. Although it can be argued that any indicator is simply a proxy for some underlying construct, the choice of appropriate indicators is further limited in a study using secondary data analysis. Reliability tests are in line with pre-measurement model construct assessment (Byrne, 2001), however, the final set of measures may not have accurately represented the intended constructs of interest. For example, indicators for the social bond construct only measured the attachment component. It is unclear whether the use of these three indicators of attachment was sufficient to adequately tap into the concept of adolescent social bond. Construct validity concerns also arose from the lack of association between several measures and the problem behaviors construct. For example, current results revealed a significant relationship between anger control and problem behaviors among female and rural respondents. It is unclear however, whether the association between these two constructs is driven by the aggression component of problem behaviors or the entire three-part structure. Future studies can parse
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this potential gender-problem-behavior interaction using three outcome factors (delinquency, aggression, and drug use) instead of a singleorder construct. By separating the different components of problem behaviors into three separate constructs (Farrell et al., 2000), one can test the relationship between an explanatory factor (e.g., anger control) and the individual components of the problem behavior measures.47 The results from these analyses may explain whether lower aggressive tendencies among female and rural adolescents are associated with serious aggression-related problem behaviors or all forms of problem behaviors including delinquency and drug use. Sample size. The sample size (n =1286) was more than adequate for testing a single sample model. Sample size also was adequate for male/female and rural/urban group comparisons (see Appendix F). The sample size did not allow for mutually exclusive gender by location groupings, precluding any 2x2 interaction tests between the gender and location moderators. All the same, results revealed both variables were independent of each other such that a proportionately equal number of males and females could be found in the rural and location groups. Future tests of the current model should allow for adequate sample sizes of 500 cases per group; the equivalent of 2000 cases for replication of the current study with four levels of moderation. Generalizability School-Location Effects. A potential limitation to the generalizability of this study stems from the data collection schedules. Time lags in data collection resulted from differences in funding schedules, school site visits, and coordination among the investigators, such that information was collected between the Spring of 1998, 2000, and 2001. Although these data are comprised entirely of 8th grade students at the respective sites, historical effects in these separate cohorts may impact the response pattern of measures that relate to problem behaviors. For example, data collected from the California school sites after the highly publicized Columbine school shootings in 1999 may overestimate perceptions of neighborhood risk and delinquency related to violence. 47
The outcome measure need not be a second-order model. It can be three single-order models with pathways leading from the explanatory factors to each outcome construct (delinquency, aggression, and drug use).
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In addition, data were collected from 8th grade students in Nevada in 1998 and 2001 from the same school site. Factors common to the school site which were not captured in this study may have led to systematic responses that do not generalize to other populations. Modified Models and Sample Specificity. Results showing out of range fit indices and non-significant hypothesized pathways in the original model indicated that the hypothesized model did not adequately represent the patterns in the data. A modified model was developed using the pathways that were statistically significant in the original model. Pathways that did not connect to the problem behavior construct were omitted from the modified model. Since the modified models originated from results that were specific to the sample in this study, the results do not generalize to other populations. Implications for Research and Prevention Efforts Adapted Social Systems Framework. The results of this study have several implications for future research and problem behavior prevention efforts. To develop the hypothesized model for this study, an adapted social systems framework was created. The adapted framework combined Bronfenbrenner’s (1979) ecological model of human development, Jessor and Jessor’s (1977) Problem Behavior Theory and Bartol and Bartol and Bartol’s (1989) infra-system. This adapted framework provides a conceptual distinction between psychological (infra-system) and behavioral (behavior system) outcome models that are based on a social systems perspective. In this framework, personal characteristics (e.g., personality, genetics) are viewed as filters that mediate external (e.g., interpersonal and contextual) forces that affect behavior (e.g., problem behaviors in this study). The separation between personal (infra-system) and behavioral outcomes has important implications for research and prevention. Assessing both personal and behavioral outcomes allows for the detection of emerging or unpredicted relationships that may go unnoticed. Recent literature challenging the widely accepted association between low self-esteem and problem behaviors, are an example (Baumeister, Bushman, & Campbell, 2000; Baumeister & Smart, 1996; Bushman & Baumeister, 1998).
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A similar lesson can be derived from the results in this study. For example, previous research has linked internal locus of control to fewer problem behaviors among high school boys (Jessor & Jessor, 1977). In the current study, internal locus of control was negatively associated with problem behaviors among rural adolescents while no association was found among male, female, and urban youth. Limiting the outcome measures only to personality variables would have provided an incomplete picture of adolescent well-being in these samples. An intervention program for example, that focused solely on improving (i.e., internalizing) locus of control would have overlooked the lack of association between locus of control and problem behaviors in the three samples. Such a program may have successfully improved internal locus of control but done little to reduce problem behaviors among urban adolescents. Objective Measures. The modified framework used in this study relied on the importance of the perceived environment to predict behavior (Bronfenbrenner, 1979; Jessor et al., 1977). It was argued that respondents’ perception of the level of risk in their community and how much their parents monitored them may be a stronger predictor of problem behaviors than actual levels of risk in the community and parental accounts of parental monitoring. Parental monitoring, for example, is only effective in reducing problem behavior when parents actually monitor their children and prevent the behavior from occurring. To the extent that adolescents seek to avoid parental discipline, the perception of parental monitoring may be more effective in reducing problem behaviors than actual parental monitoring. Nevertheless, future research could assess more diverse (e.g., coded video data) measures of parental monitoring. Such assessments could elucidate processes through which perceptions of monitoring are established, maintained, and reinforced. Similarly, capturing official crime statistics on the level of community risk (e.g., number of crimes or arrests in a given county), would allow researchers to determine whether local crime statistics are consistent with adolescents reported exposure to crime. Official measures of community risk also could be combined with the current indicators of risk to create a multi-item construct of neighborhood risk. At a conceptual level, such a construct could capture the dual impact that neighborhood crime has an adolescent behavior via exposure to actual crime and concerns about imminent victimization.
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Gender. The results revealed that anger control was associated with problem behaviors among females but not males. As noted earlier, Chesney-Lind has suggested that most problem behaviors are trivial, generally amount to status offenses, and that this pattern is more indicative of female problem behaviors (Chesney-Lind, 1989; Klein, 1973). In this study, the inclusion of status offenses as part of the outcome measure was expected to improve the model’s relevance to the explanation of female problem behaviors. This expectation was indirectly supported by results that revealed that the current model accounted for over fifty percent of the variance in problem behaviors among females (.522), substantially more than that accounted for males (.388). Future studies could explore whether this association reflects a relationship between anger control and a subcomponent of problem behaviors (i.e., delinquency, aggression, or drug use). Results from future studies could elucidate how the ability to controls one’s anger among females, is related to their participation in different types of problem behaviors. Another suggestion by Chesney-Lind (1989) is the inclusion of macro-level variables (e.g., paternalism) that capture how societal gender roles impact the reason why girls engage in minor problem behaviors (e.g., running away) and escalate to more serious behaviors (i.e., drug use, prostitution, and violence). Although the adapted framework used in this study can accommodate the inclusion of macrolevel factors within the macro-system, additional work is required to adequately operationalize such a distal variable. Future studies should include variables that have been identified (Chesney-Lind, 1989) as part of the process of female problem behaviors, including: victimization, sexual assault, previous delinquency, family structures (i.e., step families), views on gender roles, aspirations, and views of self. Location. The results revealed that anger control was associated with problem behaviors among rural but not urban respondents. As with females, future studies could explore whether anger control is associated with a subcomponent of problem behaviors (i.e., delinquency, aggression, or drug use). Results from future studies could explain how the ability to controls one’s anger is related to participation in different forms of problem behaviors, among rural and urban adolescents.
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The hypothesized model accounted for more variance in problem behaviors among rural respondents (.566) than urban respondents (.404). In general, location patterns mimicked gender patterns although subsequent analyses revealed no significant association between gender and location. Future studies could divide the sample into mutually exclusive demographic groups (e.g., male/rural, male/urban, female/rural, and female/urban) in order to eliminate any potential interactions. Finally, future studies could include more diverse measures of location, including population size and access to community resources (e.g., mass transit, number of hospitals, etc.). Race. Race or ethnicity was not addressed in this study. Racial differences in problem behaviors may actually reflect more distal factors such as economic and neighborhood quality variation that may correlate with race (e.g. African-American and urban). That, however, is an empirical question. Future studies could compare the potential moderating role of race to determine if it may be adequately captured by other potential moderators such as socioeconomic status, neighborhood quality, and economic opportunity. Since the latter factors are more malleable than race, they provide more promising avenues for intervention. Resiliency, Moderation Effects, and Longitudinal Studies. Resiliency has been defined as “a process of, capacity for, or the outcome of, successful adaptation despite challenging and threatening circumstances (Gamerzy & Masten, 1991, p. 459).” This definition implies a temporal order whereby one is exposed to risk and is later able to experience a positive outcome in spite of the risk. When the transition from exposure to risk to a positive outcome is facilitated by another variable, we refer to the variable as a protective factor. The cross-sectional nature of this study, however, precludes any claims at causation of any positive outcomes (i.e., resiliency). To address this, future studies could examine the current model using longitudinal data. Alternative approaches to the assessment of resiliency may provide new avenues for testing the current model of problem behaviors. Zimmerman and Arunkumar (1994) describe three models of resiliency: compensatory, challenge, and protective factors models. Compensatory models also are known as additive models where the influence of a risk and protective factor combine (are added) to produce a given outcome. For example, high negative peer influence may be associated with high problem behaviors but that effect is neutralized by
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high parental monitoring. Future studies could explore whether risk and protective factors compensate or mediate each other. Results from such analyses may help determine why certain components of a hypothesized model were not statistically significant. The challenge model suggests that exposure to some risk can be helpful in “steeling” an individual to better handle stressors in the future (Zimmerman and Arunkumar, 1994). The challenge model can be tested using the hypothesized model in this study. The aftermath of the recent tragedy involving the murder suicide of seven students and a teacher at a Native American school, can provide an example. The elevation of perceived neighborhood risk following the shooting may be mediated by increases in social bond (Path J)48 as the community comes together to deal with the tragedy. The impact of this inadvertent positive outcome may then manifest via a direct reduction in problem behavior (Path L) and indirect reductions through reduced peer influence (Path K) and internalized locus of control (Path 7). The final model of resiliency is the Protective Factors model that has two variations: the Risk/Protective model and the Protective/Protective model. The Risk/Protective model suggests that the impact of a risk factor (e.g., negative peer influence) on an outcome variable (e.g., problem behavior) is determined by the level of a protective factor (e.g., absence/presence of parental monitoring) acting as a moderator. The Protective/Protective model suggests that the effect of a protective factor (e.g., parental monitoring) on an outcome variable (e.g., problem behavior) is enhanced (moderated) by the presence of another protective factor (e.g., locus of control). In other words, parental monitoring has an attenuating impact on problem behavior. This influence is amplified among those with an internal locus of control; they are more likely to internalize the belief that problem behaviors are something they do not engage in and represents a decision they made for themselves. The infra-system in the framework used in the study can provide many potential moderating risk and protective factors (e.g., biological, personality). Factors from other parts of a social systems framework may be tested as potential moderators of a specific relationship or an 48
The pattern suggests a positive relationship which is counterintuitive but consistent with the notion of positive outcomes following a tragedy (Frazier, Conlon, & Glaser, 2001; Tedeschi, & Calhoun, 1995).
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overall process described in a model. Future studies could focus on micro- and meso-system factors (e.g., parental monitoring) that encompass a process in lieu of infra-system factors (e.g., race) that are not as malleable (Bronfenbrenner, 1979). Conclusions. The current study examined whether personal, interpersonal, and contextual risk and protective factors could adequately explain problem behaviors using a sample of 1286 eighth grade students, from five western states. Results provided limited support the original model; although a hypothesized double-mediated pathway was supported. The process of model building resulted in an adapted social systems framework that has relevant application to future research, program development, and evaluation efforts. The separation of the infra-system and behavior-system provides conceptual clarity when designing a study that focuses on exploring or fostering positive outcomes. Research studies or prevention efforts that limit their assessment (i.e., outcome measure) only to personal factors, may obtain an incomplete or misleading picture of adolescent health. Assessing personal and behavioral outcomes as suggested by the current framework, allows investigators to detect emerging or previously undetected relationships among factors related to adolescent problem behaviors. The hypothesized model accounted for over 50% of female problem behaviors. The modified model for gender accounted for a similar amount of variance in problem behaviors and obtained adequate model fit. In future studies, the integration of macro-level factors (e.g., gender roles) into the hypothesized model may help explain an additional amount of variance in female problem behaviors. The hypothesized model in this study provided limited support for the research question. Modified models, derived from the results of the original models, support the notion that social psychological factors and the perceived environment are key determinants of behavior. Present results also revealed that personal level factors can mediate the effect of distal influences on problem behaviors and that this process is moderated by gender and location. Notwithstanding the accomplishments of the current study, several avenues for future research remain. Longitudinal assessments offer a fruitful and logical next step for further tests of the given framework and model. Future studies can explore whether the hypothesized model
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in this study can adequately describe problem behaviors at different stages of adolescence. Although the current study did not propose to test a model of resiliency directly, the adapted framework and results derived from this project fit the stream of resiliency research. Longitudinal studies, interventions, and program evaluations can be developed using the adapted framework used in this study and the resiliency models discussed earlier. Finally, a comparison of the adequacy of the current model and alternative models of adolescent problem behaviors will be critical in determining the contribution of this study to the overall literature on adolescent health.
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Appendix A: Survey Origins & Objectives
This study was supported in large part by the award from USDACSREES for Cooperative Regional project W-193 (Resilience to Violence Among At-Risk Youth). The project allowed investigators from participating institutions to apply for funding for travel, planning, and data collection costs associated with developing and administering the Teen Safety Survey, discussed below. The immediate objective of the W-193 Project was to identify protective factors within a temporal/ecological model that are associated with adolescent peer-related perpetrator and victim violence. This includes the isolation of protective factors which directly reduce or offset risk, as well as factors that enhance other protective factors, thereby reducing the probability of violence or traumatizing effects of victimization. For additional information on the W-193 Project, please contact the Chair, Bill Evans, Department of Human Development and Family Studies, University of Nevada, Reno, Mailstop #140, Reno, NV 89557; e-mail:
[email protected].
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Appendix B: Arrestable Offenses
ARRESTABLE OFFENSES Table B1 lists the offenses for which adolescents may be arrested and prosecuted. Table B2 lists additional offenses in the Juvenile code. Definitions for a majority of offenses is provided at the end of this section. Table E1 Offenses For Which an Adolescent May Be Arrested and Prosecuted Violent Crime Index 1. Murder and non-negligent manslaughter 2. Forcible rape 3. Robbery 4. Aggravated assault Property Crime Index 5. Burglary 6. Larceny-theft 7. Motor vehicle theft 8. Arson (Continued on the next page)
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Table B1 Offenses For Which an Adolescent May Be Arrested and (continued) Prosecuted 9. Other assaults 10. Forgery and counterfeiting 11. Fraud 12. Embezzlement 13. Stolen property; buying, receiving, possessing 14. Vandalism 15. Weapons; carrying, possessing, etc. 16. Prostitution and commercialized vice 17. Sex offenses (except forcible rape and prostitution) 18. Drug abuse violations 19. Gambling 20. Offenses against the family and children 21. Driving under the influence 22. Liquor laws violations 23. Drunkenness 24. Disorderly conduct 25. Vagrancy 26. Suspicion 27. Curfew and loitering law violations 28. Running away from home Source: FBI (2002). Uniform Crime Reports
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Table B2 Delinquent Offenses as Described in the Juvenile Code 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
Violating of any law or ordinance Violating a juvenile court order Associating with criminal or immoral persons Engaging in any calling, occupation, or exhibition punishable by law Frequenting taverns or uses alcohol Wandering the streets in the nighttime Growing up in idleness or breaks curfew Entering or visiting a house of ill repute Being habitually truant Being habitually disobedient or refusing to obey reasonable and proper (lawful) order of parents, guardians, or custodians Engaging in incorrigibility or ungovernability Absenting himself or herself from home without permission Persisting in violating rules and regulations of school Endangering the welfare, morals, and /or health of self or others Using vile, obscene, or vulgar language (in a public place) Smoking cigarettes (around a public place) Engaging in dissolute or immoral life or conduct Wandering about railroad yards or tracks Jumping a train or entering a train without authority Loitering, sleeping in alleys Begging or receiving alms (or is in the street for that purpose)
Source: Bartollas (2003) DEFINITIONS The following definitions are noted, verbatim, from the FBI’s (2001) Uniform Crime Reporting Program. Offenses are classified into two groups, Part I and Part II. According the FBI, each month, contributing agencies submit information on the number of Part I (Crime Index) offenses known to law enforcement; those cleared by arrest or exceptional means; and the age, sex, and race of persons arrested. Contributors provide only arrest data for Part II offenses.
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Part 1 Offenses Criminal homicide Murder and nonnegligent manslaughter: the willful (nonnegligent) killing of one human being by another. Deaths caused by negligence, attempts to kill, assaults to kill, suicides, and accidental deaths are excluded. While manslaughter by negligence is a Part I crime, it is not included in the Crime Index. Forcible rape The carnal knowledge of a female forcibly and against her will. Rapes by force and attempts or assaults to rape regardless of the age of the victim are included. Statutory offenses (no force used—victim under age of consent) are excluded. Robbery The taking or attempting to take anything of value from the care, custody, or control of a person or persons by force or threat of force or violence and/or by putting the victim in fear. Aggravated assault An unlawful attack by one person upon another for the purpose of inflicting severe or aggravated bodily injury. This type of assault usually is accompanied by the use of a weapon or by means likely to produce death or great bodily harm. Simple assaults are excluded. Burglary (breaking or entering) The unlawful entry of a structure to commit a felony or a theft. Attempted forcible entry is included. Larceny-theft (except motor vehicle theft) The unlawful taking, carrying, leading, or riding away of property from the possession or constructive possession of another. Examples are thefts of bicycles or automobile accessories, shoplifting, pocketpicking, or the stealing of any property or article which is not taken by force and violence or by fraud. Attempted larcenies are included.
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Embezzlement, confidence games, forgery, worthless checks, etc., are excluded. Motor vehicle theft The theft or attempted theft of a motor vehicle. A motor vehicle is selfpropelled and runs on the surface and not on rails. Motorboats, construction equipment, airplanes, and farming equipment are specifically excluded from this category. Arson Any willful or malicious burning or attempt to burn, with or without intent to defraud, a dwelling house, public building, motor vehicle or aircraft, personal property of another, etc. Part II Offenses Other assaults (simple) Assaults and attempted assaults where no weapons are used and which do not result in serious or aggravated injury to the victim. Stolen property; buying, receiving, possessing. Buying, receiving, and possessing stolen property, including attempts. Vandalism Willful or malicious destruction, injury, disfigurement, or defacement of any public or private property, real or personal, without consent of the owner or persons having custody or control. Attempts are included. Weapons; carrying, possessing, etc. All violations of regulations or statutes controlling the carrying, using, possessing, furnishing, and manufacturing of deadly weapons or silencers. Attempts are included.
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Prostitution and commercialized vice Sex offenses of a commercialized nature, such as prostitution, keeping a bawdy house, procuring, or transporting women for immoral purposes. Attempts are included. Drug abuse violations State and/or local offenses relating to the unlawful possession, sale, use, growing, and manufacturing of narcotic drugs. The following drug categories are specified: opium or cocaine and their derivatives (morphine, heroin, codeine); marijuana; synthetic narcotics— manufactured narcotics that can cause true addiction (demerol, methadone); and dangerous nonnarcotic drugs (barbiturates, benzedrine). Offenses against the family and children Nonsupport, neglect, desertion, or abuse of family and children. Attempts are included. Driving under the influence Driving or operating any vehicle or common carrier while drunk or under the influence of liquor or narcotics. Disorderly conduct Breach of the peace. Vagrancy Begging, loitering, etc. Includes prosecutions under the charge of suspicious person. Curfew and loitering laws (persons under age 18) Offenses relating to violations of local curfew or loitering ordinances where such laws exist.
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Runaways (persons under age 18) Limited to juveniles taken into protective custody under provisions of local statutes.
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Appendix C: Preliminary Analyses
Prior to conducting multivariate analyses, several assumptions about the characteristics of the data were examined, including: data accuracy, sample size, missing data, outliers, univariate and multivariate normality, homogeneity of variance, homogeneity of variancecovariance matrices, and multicollinearity (Tabachnick & Fidell, 1996). The potential impact of various data characteristics on statistical analyses and their interpretation are discussed, below. After an examination of the data, the manner in which any violation was addressed also is discussed. The last section of this appendix contains the results of exploratory factor analyses and scale reliabilities for each latent construct in the study. These analyses served a dual purpose. First, factor loadings and scale reliabilities scores were used to reduce the number of indicators per construct to three. Reducing the number of items per indicator was important when estimating the structural model in this study; the low number of indicators per construct allowed for good model fit without necessarily sacrificing the construct validity of the measures. Second, the factor loadings and scale reliabilities were used to determine whether the measures had adequate internal consistency and a singlefactor structure. The final set of indicators derived from these analyses were described in Chapter 3 and used in the structural equation modeling analyses, discussed in Chapter 4.
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ASSUMPTIONS Accuracy of the Data File An initial review of the data file, using the SPSS frequencies procedure, showed no inaccurate entries or responses out of the expected range49 across all (boys, girls, rural, urban) data sets. Any inaccurate entries would have been checked against the original paper survey response, logged, and corrected in the data file. Sample Size The size of the sample does not constitute a multivariate assumption nor is there a strict rule regarding the number of cases necessary for any given model. There are however, suggested guidelines that can be followed, provided there is an understanding of their impact on overall model fit. For example, Comrey and Lee (1992)50 provide the following classification index regarding adequate sample sizes: 50 cases (very poor), 100 cases (poor), 200 cases (fair), 300 cases (good), 500 cases (very good), and 1000 cases (excellent). Similarly, Boomsma (1983) recommends a minimum of 300 cases for simple to moderately complex models. These guidelines seem to suggest a “more is better” approach to the issue of sample size. Indeed, large samples size can be robust against deviations to normality (Tabachnick & Fidel, 1996). Large sample sizes however, also are likely to result in statistically significant chi-square values. Large chi-square values reflect significant differences between the structure proposed in the hypothesized model and patterns found in the actual data (Tabachnick & Fidel, 1996). In contrast, small or inadequate sample sizes are likely to yield erroneously good-fitting models as a result of lack of statistical power to detect differences between the proposed model and the actual data. Bryant and Yarnold51 (1996) point out that small sample sizes in structural equation modeling, are prone to Type I errors by erroneously
49
The range of expected values for each variable was obtained from the codebook for the Teen Safety Survey Questionnaire. 50 As discussed in Tabachnick & Fidell (1995) p. 640. 51 See Bryant & Yarnold, p.117
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failing to reject models on the basis of non-significant chi-square scores. Some researchers have suggested a minimum of 5 to 10 cases per indicator when conducting factor analysis (Bryant & Yarnold, 1996) while others have recommended using a ratio of parameters to cases (Bollen52, 1995). The most complex latent variable model in this study has 57 variables and 83 parameters to be estimated. The sample size for each of the 4 subgroups are n = 590 for boys, n = 696 for girls, n = 576 for urban adolescents, and n = 720 for rural adolescents. Across the four samples, the ratios of cases to variables, using the most complex latent variable model, are 10:1, 12:1, 10:1, and 12:1, respectively. The ratio of parameters to cases across the four samples are, 7:1, 8:1, 7:1, and 8:1, respectively. These ratios and nominal figures suggest there is sufficient data to estimate the current model across groups. Ranging between 575 and 720 cases, the sample size for each group is well above Comrey & Lee’s (1992) recommendations for a “very good” number of cases. The four sample sizes also surpass the number of cases suggested by Bollen (1995) and Boomsma (1983) and exceed the ratio of cases to indicators and cases to parameters, recommended by Bryant & Yarnold (1996). Overall, the sample size for each subgroup is more than adequate to address the research questions and hypotheses in this study. Missing Data According to Schafer & Graham (2002) the pattern of missing data can be viewed as a set of variables, R, also referred to as missingness, that have a joint probability distribution. Conceptualizing R as a set of variables is not intended to imply causality, as from a set of variables to another, but to emphasize the existence of a distribution that is concomitant with any variable. Indeed, it is the distribution of R that is of interest. Schafer & Graham describe the distribution of R as the response- or missingness mechanism that allows us to explore potential relationships between missingness (R) and the values of the missing data. A description of the different patterns of missing data helps elucidate this point. 52
See Tabachnick &Fidell, p. 641.
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There are several patterns the distribution of missing data can assume (Bryant & Yarnold, 1996; Schafer & Graham, 2002; Tabachnick & Fidel, 1996) that are based on the relationship between missingness and the missing data values (see Figure C1). If missing data occur completely at random (MCAR), it can be said that R (i.e., missingness) is caused by a set of unknown random variables (Z) that are unrelated to the data set. This relationship is depicted graphically in Figure C1a, wherein missingness is not related to the missing data themselves (Y) or any other variable (X) in the data. MCAR can occur for example, when one has missing data on a questionnaire that is caused by respondents arriving late as a result of inclement weather conditions (Z) on the day of data collection. In those circumstances, missingness is not related to the values of the missing data or to any other variable in the data set. Data are missing at random (MAR) when missingness, a) is partially caused by a random set of factors (Z), b) initially appears to be related to the missing data itself (Y), but c) is ultimately explained by some other complete variable in the data set (X). As such, data that follow a MAR pattern can also be considered as a type of nonrandom missing data. However, MAR is also referred to as “ignorable” or “accessible” because particular variables in a study may predict whether other variables will have missing scores and what those missing scores might be (see Figure C1b.). For example, consider a questionnaire assessing the prevalence of adolescent violence and victimization, administered to a group of at-risk adolescent at an afterschool program. Initially, missingness appears to be related to missing values on items assessing the number of acts of vandalism the respondent may have participated in. That is, it would appear that the pattern of missing data, on presumably incriminating items, is a function of the actual values those responses can take. Some respondents may fail to disclose their role in vandalism out of concern for self-incrimination that may lead to some penalty or punishment. However, when say, a personality measure of introversion-extraversion (X) is regressed on a dummy variable representing the missing data, we find that introversion is related to (correlated with) the missing data. Specifically, introverted respondents are less likely to answer questions regarding violence compared to extraverted adolescents. Data are not missing at random (MNAR) when the missingness is related to the actual missing values. MNAR also is referred to as “non-
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ignorable” or “non-accessible” because variables that not in the study predict whether other variables will have missing scores and what those missing scores might be. Similar to MCAR and MAR, data that are MNAR may be related to unknown random factors (Z) outside the data, and factors such as extraversion (X) within the data. However, when missing data (Y) is related to missingness above and beyond that accounted for by Z and X, the pattern of missing data is considered systemic or MNAR (see Figure C1c). Continuing with our earlier example, let us assume that we have determined that some of the missing data can be accounted for by introverted personality style. If we find that data are missing among those likely to have engaged in the(negative) behaviors, we have a non-ignorable systemic pattern of missing data. The problem is non-ignorable since the missing data is a result of the very items set up to capture the data. Missing data can be addressed by several methods: listwise estimation, pairwise estimation, mean substitution, regression, expectation maximization, multiple imputation, and full information maximum likelihood. Listwise estimation removes the entire case from subsequent analyses if any variable for that case has missing data. Pairwise estimation eliminates cases from subsequent analyses only if data are missing for the particular variables being analyzed. Listwise and pairwise estimation depend on the assumption that missingness has an MCAR pattern and missing data are rare (less than 5% of any variable). Violation of this assumption can lead to biased estimates as available cases are overrepresented while the reduced sample size and statistical power hinder detection of potential underlying relationships in the data. The concerns regarding case deletion are exacerbated in multivariate analyses, where acceptable levels of univariate deletion (5% of less) can lead to large number of cases being deleted. Analyses of the current data shows that case deletion would result in a decrease in sample size of 23%, from 1286 to 989 cases. An alternative to deleting cases, mean substitution, belongs to a family of missing data techniques involving imputation. Means substitution, involves computing the mean of a variable from cases that are not missing data and replacing the missing data for that variable with the computed mean. Imputation techniques are attractive since they a) do not reduce sample size and statistical power, b) allow for statistical analyses that require complete data, and c) allow for some measure of precision as the imputed information is based on known
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data. However, mean substitution reduces the variance of the variable and its correlation with other variables (Graham & Shafer, 2002; Tabachnick & Fidell, 1996). To avoid these limitations and preserve some variability, hot deck imputation may be used. Hot deck imputation replaces missing data for a variable with values (for that variable) selected randomly from other respondents. This approach, while maintaining variability through random selection, does not correct issues of distorted correlations (Graham and Shafer, 2002) as the imputed values are duplicates on those already found in the data set. Regression estimation is another form of imputation that estimates missing data from available data. Consistent with Figure F1b, regression estimation depends on the assumption that missingness is MAR such that the pattern of missing data (R) is related to the observed data (X), only. For example, known values of Y may be predicted using data from variable X, which has complete information. For those cases with missing data, the fitted regression model is then used to predict the missing Ŷ(y-hat) values using data from variable X. According to Graham & Shafer (2002) however, imputation via regression overstates the relationship between the X and Y variables and thus is not recommended for analyses of covariance or correlations. To address the issue of “distorted covariances” a random residual error value can be added to the predicted Ŷ value. Compared to listwise and pairwise estimate, regression offers a more realistic and relaxed assumption about patterns of missingness. The advantage of regression extends across imputation procedures. In simulated studies, Graham & Shafer, (2002) reported that regression estimation was unbiased under conditions of MCAR and MAR but could be biased under MNAR. On the other hand, mean substitution and hot deck estimation procedures were biased under all patterns of missingness. Multiple imputation (MI) and maximum likelihood estimation (MLE) procedures provide additional advances to simple regression approaches under the similar assumption of MAR. When using MI, several (m > 1) simulated values for each missing data item are computed. Data are then analyzed under conditions of complete information, for each set of imputed variable values. The results are then added and used “to obtain overall estimates and standard errors that reflect the missing-data uncertainty as well as finite sample variance (Graham & Shafer, 2002).” MI has several benefits: a) data
Figure C1. Patterns of Missing Data X
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MCAR – data missing completely at random. Missingness (R) is related to unknown variable (Z) but unrelated to variable (X) with no missing data or variable (Y) with missing data. MAR – data missing at random. R is related to Y but the relationship can be accounted for by X. MNAR – Missing not at random. Missingness is related to the missing data Y, above and beyond the role of X Adapted from original image by Graham & Shafer (2002).
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are analyzed under conditions of full information, b) it is computationally efficient as it often require few (10 to 20) iterations of data estimation, c) can be robust to non-normally distributed data and small samples53, and d) is based on data available for each case. An alternative to the MI procedure is Maximum Likelihood Estimation (MLE). The purpose of MLE is to determine the parameter estimates that make the data more likely. In other words, MLE selects parameters most likely to reproduce the observed data. While this approach appears tautological, it is based on the fact that the best estimate of a population parameter is the sample statistic. MLE also can be applied to the estimation of missing data using the expectationmaximization (EM) algorithm. Through multiple iterations, EM replaces missing data using the best (expected) estimates necessary to reproduce the population estimates (computed from the available data). The estimates are computed again using the original data and the recently imputed data. This process is repeated until the expected value of the estimate is maximized. MLE is appropriate under the assumption of MAR where missingness is related to other variables in the data, not the missing values themselves. Nevertheless, under MCAR, MLE performs exceeding better than listwise and pairwise approaches: computed estimates have smaller standard errors (and p values) as a result of creating an effectively larger sample size. The statistical software package AMOS5 uses a variation54 of MLE called, full information maximum likelihood (FIML) estimation. FIML incorporates data from observed variables (X) and the available data from variables with missing values (Y) to estimate those missing values. The inclusion of incomplete data (Y) increases the precision used to estimate the missing data, especially under conditions of MCAR (Enders & Bandalos, 2001). Wothke (2000) and Enders & Bandalos (2001) have found FIML to be the least biased estimation procedure for missing data compared to listwise or pairwise deletion under MCAR, or mean imputation under the assumption of MAR. In sum, under MCAR, mean substitution produces biased parameter estimates while listwise, pairwise, MI, and MLE produce unbiased estimates. Furthermore, MI and MLE provide greater 53 54
However, large samples are an assumption. FIML uses a different function than that used in the EM algorithm.
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efficiency than listwise and pairwise approaches due to effectively larger sample sizes. Under MAR, mean substitution, listwise, and pairwise approaches produce biased estimates while MI and MLE provide unbiased estimates and greater efficiency. Under assumptions of MNAR, all approaches produce biased estimates. However, MI and MLE remain less biased and more efficient than listwise and pairwise techniques. Since AMOS5 was used to estimate and compare the structural model in this study, it was necessary to ascertain whether the data supported the minimum assumption of MAR under FIML estimation. To assess whether the pattern of missing data for this study were MCAR, MAR, or MNAR, univariate frequencies were analyzed to determine the percentage of missing data for each variable. Analyses were performed separately for male, female, rural, and urban groups. Among male, female, and urban respondents’ data, no variable had more than 5% missing data. This is a requirement to do any listwise or pairwise deletion. However, it does not take into account multivariate missing data which would reduce the number of cases across all groups by 23.1% from 1286 to 989. Rural respondents however, had over 5% (but less than 6.3%) missing data for the following variables: SCAL10C – “In our family, people hardly ever lose their tempers.”, SCAL10DR – “In our family, people sometimes hit each other.”, SCAL10E – “In our family, people rarely criticize each other.”, SCAL11AR – “I believe there is really no way I can solve some of the problems I have.”, SCAL11BR – “Sometimes I feel that I'm being pushed around in life.”, SCAL11CR – “I have little control over the things that happen to me”, SCAL11D – “I can do just about anything I really set my mind to do”, SCAL11ER – “I often feel helpless in dealing with the problems of life”, SCAL11F – “I believe that what happens to me in the future depends mostly on me”, and SCAL11GR – “I believe there is little I can do to change many of the important things in my life”. It appears that rural respondents were less likely to provide responses to items related to family conflict and locus of control. Means and standard deviations were computed using 4 different procedures for handling missing data: listwise, pairwise (i.e., all available cases per variable), estimation-maximization, and regression. Differences in the values of the means and standard deviations across
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the four estimation procedures were negligible or non-existent. This result was similar across the four subgroups. The lack of difference in means and standard deviations across estimation techniques suggests that data may not be missing in any systematic fashion. Specifically, population estimates appear to be unaffected regardless of whether cases with missing data are removed from analysis or the cases are retained and the missing data is estimated. While this finding suggests that cases can be removed listwise, it also affords the opportunity to retain all cases and use EM procedures. By retaining all cases, one benefits from the increased efficiency and accuracy of a larger sample size. To assess whether data are missing completely at random, the SPSS missing data analysis provided Little’s Test for MCAR in the output for EM estimation. The results show that the hypothesis that the data are MCAR was not rejected for the male (χ2 = 96.392, df = 3562, p = 1.000), female (χ2 = 96.392, df = 3562, p = 1.000), rural (χ2 = 82.268, df = 3080, p = 1.000), and urban (χ2 = 101.853, df = 4090, p = 1.000) groups. Since the SEM analyses using AMOS5 utilize the FIML estimation procedure, these results suggest that the MCAR data may allow for better efficiency in estimation. Given that data were found to be MCAR across all four groups, no further tests were required. Univariate and Multivariate Outliers Using computed z scores, outliers were examined for all variables across the four subgroups. Z scores above 3.0, or three standard deviations away from the mean, were considered outliers. The results identified several variables with outliers. However, further examination and consideration led to the decision not to remove these cases. The range of possible scores for the continuous variables in the data set is rather limited (e.g., 0, 1, 2, 3, and 4). This limited range in scores is likely to result in values that can be labeled outlier though they represent an expected portion of the responses. For example, when assessing indicators of problem behaviors, which are often nonnormally distributed among respondents in general populations, the mean for that indicator will tend to be near the response value indicating no participation. Respondents indicating little or moderate participation in problem behaviors are then likely to present themselves
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as outliers. The purpose of this study is to detect the full range of behaviors (i.e., responses) in a general, albeit, at risk population. Removal of statistical, but not theoretical, outliers would reduce the power and precision of the study. Thus, no cases were removed from analysis based on extreme response scores. Multivariate outliers often occur when at least one of two variables contain univariate outliers. Given the decision to retain univariate outliers, no multivariate outliers, identified through Mahalanobis distances were removed. Univariate and Multivariate Normality Normal distributions are symmetric about the mean as their mean, median, and mode are approximately equal to each other. Deviations from normality can result in degraded statistical solutions, especially if the violations are in opposite directions across variables (Tabachnick & Fidell, 1996). Skewness and kurtosis are two components of normality and are equal to zero in normal distributions. Skewed distributions are not symmetrical and their mean is not in the center. Negatively skewed distributions have a large portion of observation on the right and a long tail that trails to the left. Positively skewed distributions have the majority of observations on the left and a long tail that trails to the right. Kurtosis refers to the peakedness and flatness of the distribution. Negatively kurtotic distributions are flat and have most of their values around the thin tails. Positively Kurtotic distributions are too peaked and have heavy tails. When superimposed over a normal distribution with or equal variance, the sides of kurtotic distribution will cross normal distribution twice. While some researchers have argued that kurtosis underestimates the variance (Tabachnick & Fidell, 1996), others suggest kurtosis need not, and often does not, affect the variance as it merely reflects a shift in density (DeCarlo, 1997). If data are not normally distributed, transformations can be performed. However, if the behavior is presumed not be normally distributed (e.g., drug use), one can select an estimation method that addresses non-normality. Maximum likelihood (ML) and Generalized Least Squares (GLS) are robust against violations to normality with sample sizes above 2,500. GLS performs well with samples below this number but can lead to the “acceptance of too many models (p.752).” ML-based estimation
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procedures (i.e., FIML) were appropriate for this study where sample sizes ranged between 575 and 720 cases and non-normally distributed data. Skewness and kurtosis were computed for each of the variables in the study. In addition, z-statistic55 and significance levels56 were also computed for skewness and kurtosis using the equation provided by Tabachnick & Fidell (1996). Items relating to problem behaviors, negative peer influence, family conflict, parental monitoring, and (some) social bond showed consistent problems with extreme skewness across all four subgroups. The extreme instances of kurtosis (at the p < .001 level) were evident in items relating to problem behavior. Histograms showed distribution patterns similar to those depicted by the significance tests. Q-Q probability plots of the distribution of each variable against a normal distribution also showed a lack of linearity among problem behavior items, especially questions dealing with drug use. Despite the differentiated results noted above, univariate analysis of normality showed statistically significant deviations from normality across all variables (p < .001 for all variables). The Shapiro-Wilk omnibus test also indicated overall deviations from normality at the univariate level. Data also were analyzed using a SPSS script developed by DeCarlo (1997) for testing univariate and multivariate skewness and kurtosis. Results indicated significant skewness and kurtosis across most variables including all problem behavior items. However, the aforementioned tests are conservative and likely to indicate minor deviations from normality as significant. 55
56
SD and z-statistic for Skewness
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Using the .001 level accounts for family-wise error. A significance level of.05 divided by 48 variables results in a family-wise level of .00104.
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Solutions to violations of normality may be addressed by transformations. Positively skewed distributions were transformed using the square root57 transformation while negatively skewed distributions were transformed using the square root with a subtracted constant58. Post transformation results across all four groups showed little improvement in skewness for items relating to social bond, negative peer influence, and parental monitoring and almost no improvement in family conflict and problem behavior items. On the other hand, the transformation had a net worsening effect on kurtosis: the number of items with extreme kurtosis increasing across all four groups. Despite the concerns raised by these violations to normality, the distribution patterns for variables in this study are theoretically consistent with the behaviors they capture. As noted earlier, for example, one expects the prevalence of delinquency, aggression, and drug use to occur in a minority of the population. Second, one also would expect the frequency of these behaviors to be high among this minority. These two aspects of problem behaviors result in skewed and kurtotic distributions. While these variables, with non-normal distributions, are prime candidates for transformation, they benefit marginally from such transformations. Furthermore, the loss of interpretability that comes with a transformation would only be exacerbated by the highly complex model with multiple levels of mediation. Given these concerns, no transformed data were retained for subsequent analyses. Instead, greater reliance was given to FIML estimation procedure to be robust against the aforementioned deviations from normality. Homegeneity of Variance Homoskedasticity or homegeneity of variance (for grouped data) is the assumption that the variability of scores in the dependent variable is equal across different levels of the grouping variable (Tabachnick & Fidell, 1996). In relation to this project, a test of homoskedasticity 57
The transformation is of the form where the New Variable = SQRT(Old Variable). 58 The transformation is of the form where the New Variable = SQRT(Constant - Old Variable).
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would tell us whether the variance of a variable is equal among boys and girls or rural and urban samples. Homegeneity of variance is determined by a significant score in a Levene test or a value over 10 using the less conservative F-Max test. The Fmax test is applied when dealing with non-normal data and unequal sample sizes.59 The presence of homogeneity of variance can be corrected using transformations (Tabachnick & Fidell, 1996). Given the sensitivity of the Levene test, the F-Max test also was computed to determine if any transformations were necessary. Homoskedasticity was examined across gender and location. Among males and females, some items related to Social Bond (3), Internal Locus of Control (3), and Problem Behaviors (6) had significant (p < .001) scores on the Levene test but no item had a score greater than 3.6 on the F-Max test. Among rural and urban respondents, some items related to Social Bond (3), Negative Peer Influence (1), Anger Control (1), Family Conflict (1), Neighborhood Risk (1), and Problem Behaviors (11) had significant (p < .001) scores on the Levene test but no item had a score greater than 3.5 on the F-Max test. The low scores on the F-Max test for both gender and location suggested that corrective transformations were not required. Homogeneity of Variance-Covariance Matrices The assumption of homogeneous variance-covariance matrices suggests that an entry in a variance-covariance matrix for one level of a dependent variable is equal to the same entry for another level of the dependent variable. For example, consider a situation where the dependent variable, gender, is nonmetric (e.g. male or female) and there are two or more metric independent variables (e.g., social bond, delinquency, anger control, etc.). Some statistical techniques assume that the variances and covariances of the independent variables (e.g., social bond, delinquency, anger control, etc.) are similar across gender groups. When using multivariate statistical procedures (e.g. MANOVA, Discriminant Analysis), SPSS provides the Box’s M test to determine whether variance-covariance matrices are homogeneous.
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Discrepancies in sample sizes can be as large as 4 to 1 for use of the Fmax test.
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In structural equation modeling, multi-group comparisons follow a similar logic to tests of homogeneity of variance. The hypothesized (structural) model, denoted by the variance-covariance structure of all related variables, is compared across the levels of another, usually nonmetric, variable. Each level of the non-metric variable represents a group or subsample in the data. The structural weights and residuals computed from these variance-covariance matrices are simultaneously estimated to determine if they are similar (i.e., invariant) across groups. In this project, the hypothesized model is compared among male and female, and rural and urban respondents, to determine whether the hypothesized model of problem behaviors accurately reflects the data in each group. Thus, given the similarities in tests of homogeneity of covariance matrices and tests of invariance in multi-group SEM, no test of homogeneity of variance-covariance matrices was performed. Multicollinearity Multicollinearity occurs when two variables have a correlation of .90 or above. When variables are identical, they are called singular. When variables are singular, it is difficult to discern how much variance each variable accounted for in the final solution. Multicollinearity can be detected by examining the correlation matrix for relationships above .70. The only way to correct issues of multicollinearity is to remove one of the variables from the analysis. Correlations among all 48 variables were examined for multicollinearity among the male, female, rural, and urban groups. No pair of variables had a correlation greater than or equal to .70, thus no variables were eliminated. DATA REDUCTION Rationale Structural equation modeling (SEM) was used to estimate the hypothesized model in Figure 2.7. One of the advantages of SEM over other statistical procedures (e.g., regression and path analysis) is the ability to use multiple items to represent an underlying construct. Indeed, SEM offers the ability of estimating models that include both, single-item indicators and multi-item constructs. In general, however, multi-item constructs are preferred over single-item measures.
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Measurement theory suggests that the greater the number of relevant indicators, the better one measures the construct underlying all of those indicators (Nunnally, 1967). The notion is analogous to the accuracy with which one can describe an object when one is able to view it from different angles as opposed to an observation made through a single vantage point. In SEM, a measurement model represents the description of a construct (i.e., object) using multiple indicators (i.e., vantage points). Statistical fit indices are then used to assess whether the indicators adequately describe their underlying construct. For a given measurement model, a large number of indicators may be used to attain adequate construct validity without compromising model fit. A latent variable (LV) model is comprised of several measurement models linked via theoretically specified pathways. Similar to measurement models, latent variable models also are assessed with fit indices, to determine whether the proposed pathways are supported by the data. While measurement models may contain many relevant indicators that allow for adequate (measurement) model fit, a large number of indicators can pose threats to model fit when all of the measurement models are estimated as part of the full latent variable model. Specifically, it becomes increasingly difficult to account for (i.e., reproduce) the shared variance among all of the indicators in a LV model when, a) there is a high ratio of indicators per construct, and 2) the variance for those indicators is funneled solely through a single pathway linking the latent constructs. Reducing the number of indicators increases the likelihood that the model will be reproduced by the data. Nevertheless, arbitrarily removing indicators, without theoretical and statistical justification, can result in a misspecified model with detrimental effects on model fit. For this study, the number of indicators used to represent an underlying construct was limited to no more than three items. The decision to limit the number of indicators was intended to strike a balance between measurement accuracy and overall model fit. The use of two indicators would have resulted in an under-identified model with insufficient degrees of freedom to estimate the model (Kenny, Kashy, Bolger, 1998). Using more than three indicators was not possible for all measurement models as some measurement scales had only three indicators to begin with. Using three indicators per construct however, allowed for just-identified models. Just-identified models have
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sufficient degrees of freedom to estimate the factor loadings of the latent constructs on its indicators, but not enough to enough to provide estimates of model fit. Without fit indices, it is impossible to determine whether the factor structure of the measurement model is consistent with the data. An alternative approach to individual measurement model estimation has been used by several researchers (Gottfredson, 1996, 1997; Rosay, Gottfredson, Armstrong, and Harmon, 2000; Fleming, Catalano, Oxford, and Harachi, 2002). Fleming et al. argue that researchers are ultimately interested in how individual measurements models perform when they are estimate together as part of the LV model. The authors suggest estimating all measurement models as a single combined measurement model (CMM). This procedure is performed by inserting all of the measurement models into one model where all of the latent factors are set to covary with one another. The CMModel approach provides three benefits for this project. First, the additional degrees of freedom obtained by simultaneously estimating the individual models allow AMOS5 to compute fit indices for this CMModel. Recall, that fit indices could not be computed for individual measurement models with three indicators, making it impossible to discount or accept the model. However, when the measurement models are estimated simultaneously, the additional degrees of freedom allow for an assessment of fit for the overall model. While the fit indices for the CMModel may not identify which individual measurement models are causing lack of fit60, they do provide an overall assessment of how the measurement models function together. Second, the CMM approach simplifies tests of multi-group invariance. Since only one CMModel is estimated instead of seven, the amount of multi-group comparisons is reduced considerably. This is especially relevant for this study which compares model fit across male/female and rural/urban groups. 60
The Lagrange Multiplier (LM) test can indicate which factor loadings can be removed to improved fit. This approached also is used at the LV model level to improve model fit and identify any misspecified models. The LM test only is available however, when there are no missing data. Thus, it could not be used here. As noted above however, factor loadings from SEM and EFA results and item impact on reliability scores can serve as potential guidelines for identify weak or noninvariant items.
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Third, by setting the latent constructs in the CMModel to covary, AMOS5 provides a correlation matrix for the latent constructs. This output allows us to examine the magnitude and direction of the correlations between constructs. Given the benefits of the CMM approach over estimating individual just-identified measurement models, a combined measurement model was used to estimate the adequacy of the measures and test their invariance across groups. The following section describes the procedures and results, used to select the final set of indicators for the constructs in this study. Measures with exactly three indicator items were not modified61. For measures with more than three available indicator variables, the strongest three items were selected. Selection was based on a) each item’s factor loading using exploratory factor analysis, b) its statistically detrimental impact on the Cronbach’s α if the item was deleted from a scale containing all the available items, and c) comparative face validity given the application of the underlying construct in the model. In keeping with the goal of making multi-group comparisons, the results for each measure were categorized into male, female, rural, and urban groupings. Data Reduction Procedures Analyses were conducted in two phases. In the first phase, all items were analyzed to using EFA and scale reliabilities. Exploratory factor analyses were conducted to determine which items had the highest factor loadings for their respective construct. Determining whether the measure was a single-factor structure also was of interest. The three indicators with the highest factor loadings were flagged pending subsequent analyses. Scale reliabilities were examined using all of the items for each measure. Three items, whose removal would cause the greatest decrease in the Cronbach’s α score, were selected. The items selected for their high factor loadings were compared to those with the greatest impact on the α. In general, the three items identified via factor 61
The constructs of parental monitoring and negative peer influence were not affected by this decision as they were already limited to three available indicators. Negative peer influence was later modified for different reasons, discussed later in this appendix.
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analysis and reliability estimates were the same, and the same variables selected for each of the four demographic groups. In the instances when the items identified by both methods were dissimilar, the item reporting the greatest similarity across groups was selected. Specifically, a variable was selected if it recorded the highest factor loadings and α scores across three of the four groups, or if the variable was the best indicator in each one of the grouping categories (gender and location). The purpose of this last criterion was to maintain similar indicators across the four groups. Having the same indicators represent a construct across the four groups allowed for subsequent tests of invariance that determine whether a construct is defined similarly across groups. These tests are discussed in Chapter 4. If the selection of an indicator could not be determined using the above criteria or a multi-factor structure emerged from EFA analyses, the face validity of competing indicators were compared. The purpose was not to determine whether an item had strong face validity but to determine whether one had greater face validity than another given the application of the construct in the overall model. Overall, for each construct, the three indicators with the strongest statistical relation and face validity to the underlying construct were selected for subsequent analyses. The second phase of this process involved running EFA and scale reliabilities using only the three selected indicators. The purpose of these analyses was to assess the internal consistency of the construct without the extraneous indicators. Results showed overall improvements in the internal consistency of the measures after reducing the number of indicators. The following paragraphs provide a more detailed discussion of the results used to reduce a set of items down to three indicators. The question-stems for the original set of indicators are provided, for each construct discussed below. Italicized question stems represent the final set of selected indicators. Anger Control The Anger Control subscale from the State-Trait Anger Expression Inventory consists of eight items (Spielberger, 1988). Items include the following question stems:
218
Appendix C 1. 2. 3. 4. 5. 6. 7. 8.
I control my temper. I am patient with others. I keep my cool. I control my behavior. I can stop myself from losing my temper. I calm down faster than most people. I try to be tolerant and understanding. I control my angry feelings.
Exploratory factor analysis indicated the presence of a singlefactor structure across male, female, rural, and urban groups. The results of this first round of analyses are illustrated in Table C1a. Indicators “1,” “3,” and “4,” italicized in the above list, had the highest factor loadings across all groups. These indicators are italicized in Table C1a. The amount of variance accounted by the construct was adequate at above 50%. The reliability scores obtained using the original eight items showed excellent internal consistency across the male, female, rural, and urban groups. Reliability analyses also supported the EFA results. Items “1,” “3,” and “4,” identified as having the largest factor loadings, also had the most detrimental impact on the Cronbach’s α. Given the statistical support, items “1,” “3,” and “4” were selected as the final set of indicators for the Anger Control Construct. Face validity comparisons were not required as the same indicators were identified using EFA and reliability analyses. Table C1b shows the results of a second round of EFA and reliability analyses using only these final three indicators. Results show good internal consistency with reliability scores above .8. Factor loadings were above the acceptable range of .70 while 73% of the variance in the three indicators was accounted for by the construct of Anger Control. Family Conflict The family conflict measure consists of the 5-item Conflict subscale of the Moos and Moos Family Environment Scale (FES; Bloom, 1985). Items include the following question stems: 1. We fight a lot in our family. 2. In our family, people sometimes get so angry they throw things.
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219
3. In our family, people hardly ever lose their tempers. 4. In our family, people sometimes hit each other. 5. In our family, people rarely criticize each other.
Table C1a EFA and Scale Reliability Results for the Anger Control Measure, Across Four Groups, (Initial Variable Set) Male Female Rural Urban Indicators 1 2 3 4 5 6 7 8
Factor Loadings .764 .662 .834 .781 .638 .653 .692 .669
Factor Anger Control
.760 .704 .763 .768 .639 .637 .699 .671
.761 .674 .804 .780 .680 .644 .703 .696
Total Variance Explained by Factor 57%
56%
55%
57%
.88
.89
α
Scale Anger Control
.763 .698 .787 .769 .583 .642 .693 .639
.89
.89
Exploratory factor analysis indicated the presence of a two-factor structure across male, female, rural, and urban groups. Three items, “1,” “2,” and “4,” had the highest loadings on one factor, while items 3 and 5 loaded on a second factor. This pattern was consistent across the four groups. While all five items connote a relation to anger control, items “1,” “2,” and “4” refer to more specific action-oriented manifestations of anger control. Action-oriented items may be stronger
220
Appendix C
indicators on an underlying construct if respondents draw upon behavioral history to provide their responses. The results of this first phase of analysis are illustrated on Table C2a. Factor loadings in Table C2a refer to the first factor.
Table C1b EFA and Scale Reliability Results for the Anger Control Measure, Across Four Groups (Final Variable Set) Male Female Rural Urban Indicators 1 2 3
Factor Loadings .708 .858 .819
Factor Anger Control
.718 .777 .813
.718 .798 .824
Total Variance Explained by Factor 75%
73%
74%
74%
.83
.82
α
Scale Anger Control
.706 .837 .807
.84
.81
The reliability scores obtained using the original five items showed moderate internal consistency across all groups. Cronbach’s α scores when items were deleted supported the factor analysis results. Alphas worsened when items “1,” “2,” and “4” were removed from the scale. The pattern was consistent across the four groups. The aforementioned items, italicized in the list above, were selected as the three indicators for anger control. No additional face validity comparisons were conducted given that the same indicators were identified in both phases. Table C2b shows the results of a second round of EFA and reliability analyses using only these final
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221
three indicators. Results indicate excellent internal consistency with reliability scores around .8. Factor loadings were also in the acceptable range with loadings mostly above .70 and more than 50% of the variance in the three indicators accounted by Family Conflict. Table C2a EFA and Scale Reliability Results for the Family Conflict Measure, Across Four Groups Male Female Rural Urban Indicators 1 2 3 4 5
Factor Loadings .672* .803* -.001 .719* -.156
Factor Family Conflict
.711* .749* .616 .630* .275
.613* .782* -.002 .640* .037
.658* .792* -.001 .740* -.166
Total Variance Explained by Factor 45%
47%
45%
.74
.62
α
Scale Family Conflict
50%
.65
.70
Note: All loadings refer to the second factor, except among females.
Parental Monitoring Parental monitoring measures the extent to which respondents believe their parents monitor their everyday social activities. For this study, parental monitoring was measured using three items developed by the University of Arizona’s Department of Human Development and Family Studies (K. Hoffman, personal communication, August 14, 2001). The items included the following question stems: 1. My parent(s) know where I am after school. 2. When I go out at night, my parents know where I am. 3. I talk to my parent(s) about the plans I have with my friends.
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Appendix C
Table F2b EFA and Scale Reliability Results for the Family Conflict Measure, Across Four Groups (Final Variable Set) Male Female Rural Urban Indicators 1 2 3
Factor Loadings .712 .833 .732
Factor Family Conflict
.706 .825 .698
.700 .811 .754
Total Variance Explained by Factor 72%
70%
70%
71%
.79
.80
α
Scale Family Conflict
.717 .852 .662
.80
.79
No variables were removed from this measure as it contained only three indicators. Exploratory factor analyses and reliabilities were nevertheless conducted to assess the factor structure and internal consistency of the measure. The results of these analyses are presented in Table C3. Results show good internal consistency with reliability scores ranging from the mid to low seventies. EFA indicated the presence of a single-factor structure across all groups. Factor loadings were in the acceptable range for the first two indicators, but were not as high for the third indicator. Nevertheless, over 65% of the variance in the three indicators was accounted by the Parental Monitoring construct. Negative Peer Influence The negative peer influence construct measures the extent to which an adolescent feels compelled by his/her peers to engage in behaviors he/she wishes to avoid. Peer influence was measured using three items
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223
developed by the University of Arizona’s Department of Human Development and Family Studies (K. Hoffman, personal communication, August 14, 2001). The items included the following question stems: 1. I am afraid to do things my friends don’t approve of. 2. I do things to be more popular with my friends. 3. I let my friends talk me into doing things I really don’t want to do. Similar to the parental monitoring construct, no variables were initially removed from this negative peer influence measure. Exploratory factor analyses and reliabilities were conducted to assess the factor structure and internal consistency of this measure. The results of these analyses are presented in Table C4a. EFA indicated the presence of a single-factor structure across all groups. Factor loadings however, were not in the acceptable range for the first and third indicators. The parental monitoring construct was able to account Table C3 EFA and Scale Reliability Results for the Parental Monitoring Measure, Across Four Groups (Final Variable Set) Male Female Rural Urban Indicators 1 2 3
Factor Loadings .695 .802 .603
Factor Parental Monitoring
.753 .784 .613
.756 .759 .566
Total Variance Explained by Factor 66%
67%
69%
65%
.76
.72
α
Scale Parental Monitoring
.704 .838 .662
.73
.74
224
Appendix C
between 53% and 55% of the variance in the three indicators. Cronbach’s α scores however, showed poor internal consistency with reliability scores below .60.
Table C4a EFA and Scale Reliability Results for the Negative Peer Influence Measure, Across Four Groups (Final Variable Set) Male Female Rural Urban Indicators 1 2 3
Factor Loadings .345 .809 .542
Factor Neg. Peer Influence
.510 .757 .432
.418 .886 .452
Total Variance Explained by Factor
54%
54%
53%
55%
.55
.58
α
Scale Neg. Peer Influence
.431 .705 .513
.56
.58
The three-item measure of Negative Peer Influence was later reduced to a single, observed indicator. The three-item construct began producing negative variance scores for the first and third indicators. The cause of negative variance estimates can be multi-collinearity (Kaplan, 1993). Hence, the solution in such cases is to remove the illperforming indicators which are not contributing much to the amount of variance explained. Internal Locus of Control Locus of control is a construct that measures the extent to which a person perceives that his or her actions are connected to his or her consequences (Rotter, 1966). Locus of control was assessed with seven
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225
available items from the 40-item Nowicki-Strickland Locus of Control Scale (Nowicki-Strickland, 1973). Items included the following question stems: 1. I believe there is really no way I can solve some of problems I have. 2. Sometimes I feel that I am being pushed around in life. 3. I have little control over the things that happen to me. 4. I can do just about anything I really set my mind to do. 5. I often feel helpless in dealing with the problems of life. 6. I believe that what happens to me in the future depends mostly on me. 7. I believe there is little I can do to change many of the important things in my life. Exploratory factor analysis indicated the presence of a two-factor structure across all groups. Items “1,” “2,” “3,” “5,” and “7” loaded on the first factor, while items “4” and “6: loaded on a second factor. This pattern also was consistent across groups. Given the need to select three indicators, only those items that loaded onto the first factor were considered for selection. Nevertheless, when items loading onto the first factor were ordered from highest to lowest factor loadings, they did not rank similarly across groups. In addition, while reliabilities showed moderate internal consistency, the indicators identified as having the greatest impact on the α were not always the ones with the highest factor loadings. These results were accompanied by a dismal finding indicating that only a third of the variance among the seven indicators was accounted for by the internal locus of control construct (see Table C5a). The final three indicators were selected by identifying some common threads across the factor loadings. Item “5” had a high factor loading across all groups, item “2” had the highest factor loadings for the male, female, and urban groups, while item “1” had the highest loading for the male and female groups. In order to conduct gender and location comparisons, items “1,” “2,” and “5” were selected as the final set of indicators.
226
Appendix C
Table C5a EFA and Scale Reliability Results for the Internal Locus of Control Measure, Across Four Groups (Initial Variable Set) Male Female Rural Urban Indicators 1 2 3 4 5 6 7 Factor
Factor Loadings .640 .622 .619 -.085 .634 -.107 .561
.523 .549 .600 .427 .594 .258 .567
.511 .514 .442 .581 .557 .274 .497
Total Variance Explained by Factor 36%
Scale
.572 .604 .533 .473 .629 .165 .557
36%
36%
36%
.67
.65
α
.63 .68 Note: All factor loadings refer to first factor.
Table C5b contains the results of a second round of EFA and reliability analyses using these final three indicators. Results show a slight improvement in internal consistency with reliability scores approaching .70. Factor loadings were higher, despite the fact that all but one did not surpass the acceptable level of .70. Nevertheless, the total amount of variance accounted for by the Internal Locus of Control construct surpassed 60%. Social Bond The social bond construct measures the extent to which an adolescent: a) feels an attachment to parents, teachers, or peers, b) feels committed to engaging in prosocial activities, c) is involved in prosocial activities,
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227
Table F5b EFA and Scale Reliability Results for the Internal Locus of Control Measure, Across Four Groups (Final Variable Set) Male Female Rural Urban Indicators 1 2 3
Factor Loadings .642 .693 .578
Factor
.658 .697 .590
.636 .722 .542
.675 .682 .621
Total Variance Explained by Factor 60%
61%
60%
62%
.67
.70
α
Scale .67
.68
and d) holds beliefs reflecting normative values. In the current study, positive social bond is defined as the extent to which adolescents feel other individuals (i.e., adults and family members) care about them. Social bond was measured with the 8-item Protective Factors scale from National Longitudinal Study on Adolescent Health (Resnick et al., 1997). The scale measures the extent to which participants perceive themselves as being supported by parents, teachers, and friends, and these perceptions have been negatively associated with delinquency (Morrison, Robertson, Laurie, & Kelly, 2002). Items included the following eight question stems: 1. How much do you feel teachers care about you? 2. How much do you feel other adults care about you? 3. How much do you feel parents care about you? 4. How much do you feel your friends care about you? 5. How much do you feel that people in your family understand you?
228
Appendix C 6. How much do you feel that you want to run away from home? 7. How much do you feel that you and your family have fun together? 8. How much do you feel that your family pays attention to you?
Exploratory factor analysis indicated the presence of a two-factor structure across male, female, rural, and urban groups. All items loaded highly on the first factor and very weakly on the second factor. Hence, subsequent results refer to the first factor. Similar to what had been done with the previous measures, the factor loadings for the eight items were ordered from highest to lowest, and repeated for each grouping. However, the factor loadings did not rank similarly across groups. Furthermore, while reliabilities showed high internal consistency across groups, the indicators identified as having the greatest impact on the α were not always the ones with highest (EFA) factor loadings. Table C6a contains the EFA and reliability results. While the social measure showed high internal consistency, the amount of variance explained by the construct was well below 50%. Nevertheless, a general pattern showed that items “5,” “7,” and “8” had the highest factor loadings and impact on the α, across all four groups. Items “5,” “7,” and “8” were selected as the three indicators for the measure of social bond. Table C6b shows the results of a second round of EFA and reliability analyses using only these final three indicators. Results show good internal consistency with reliability scores around .8. Factor loadings were also in the acceptable range with all but two loadings above .70. The amount of variance accounted for by the social bond construct improved, scoring above .70 across all groups. Problem Behaviors: Delinquency, Aggression, Drug Use The problem behaviors measure is comprised of three components: delinquency, aggression, and drug use. As with the previous measures, it was necessary to reduce the number of indicators, for the problem behavior measure, to three. In order to arrive at the three requisite indicators for the problem behavior construct, one indicator was allotted to the delinquency component, aggression component, and drug use component.
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229
Table F6a EFA and Scale Reliability Results for the Social Bond Measure, Across Four Groups, (Initial Variable Set) Male Female Rural Urban Indicators 1 2 3 4 5 6 7 8
Factor Loadings .600 .726 .629 .351 .696* .492 .655* .740*
Factor Social Bond
.456 .663 .618 .227 .690* .606 .723* .791*
.514 .684 .628 .293 .678* .534 .688* .772*
Total Variance Explained by Factor .45
.44
.45
.44
.81
.81
α
Scale Social Bond
.520 .695 .611 .219 .712* .600 .707* .764*
.82
.81
Reducing the number of indicator for the problem behavior construct, presented a another challenge. Each component of problem behavior (i.e., delinquency, aggression, drug use) is a construct with its own set of indicators. As previously described, however, it was decided that only one indicator could be selected for the delinquency, aggression, and drug use component. One selection option was to follow the process performed on the other constructs. The selected indicator would be the one with the highest factor loading and the largest impact on the Cronbach’s α. The best indicator for delinquency, aggression, and drug use would form the set of indicators for the problem behavior construct.
230
Appendix C
Table F6b EFA and Scale Reliability Results for the Social Bond Measure, Across Four Groups Male Female Rural Urban Indicators 1 2 3
Factor Loadings .694 .734 .818
Factor Social Bond
.706 .763 .849
.725 .752 .821
Total Variance Explained by Factor 71%
73%
72%
72%
.80
.81
α
Scale Social Bond
.674 .758 .855
.79
.81
A more robust approach involved the use of all of the available indicators. Specifically, the five indicators for the delinquency measure were summed to create a delinquency composite. A similar procedure was used to convert the three aggression indicators into an aggression composite and the five drug use indicators into a drug use composite. The three, newly created composites were then used as the indicators of problem behaviors. A description of each composite and its statistical results are discussed below. The set of questions that comprise the delinquency composite, originated from the first five items of the Resnick et al. (1997) delinquency scale. This Resnick scale measures the frequency with witch respondents participated in delinquent behaviors over the past 12 months. The first five items of this scale do not allude to interpersonally aggressive behaviors. These features made these items appropriate for creating a delinquency composite that did not tap into a separate component of aggression. The five items had the following question stems:
Appendix C 1.
231
Paint graffiti or signs on someone else’s property or in a public place. 2. Deliberately damage property that didn’t belong to you. 3. Take something from the store without paying for it. 4. Run away from home. 5. Go into a house or building and steal something. The delinquency composite was created by adding the scores for each of the five items and dividing that score by the number of indicators (i.e., five). The possible values for this composite variable ranged from one to five. Missing data for this delinquency composite accounted for 1.3% of all cases. The set of questions that made up the aggression composite, originated from a combination of two items from the Resnick scale and a violence-related item, not associated with the Resnick scale. Items numbered “6” and “7,” listed below, involved interpersonally aggressive behaviors and were appropriate for the aggression composite. The third item, which was measured along the same scale as items “6” and “7,” measured how often the respondent actually used a weapon to assault someone. Items include the following question stems: 6. Threaten to use a weapon to get something from someone. 7. Take part in a fight where a group of your friends was against another group. 8. Used a weapon (e.g., knife, gun, etc.) to threaten or assault someone. The aggression composite was created by adding the scores for each of the three items and dividing that score by the number of indicators (i.e., three). The possible values for this composite variable ranged from one to five. Missing data for this delinquency composite accounted for 1.6% of all cases. The set of questions that comprise the drug use composite, originated from a multi-item drug use scale developed by Evans (1992) which measures how often respondents used the following drugs in the last 6 months: 9. Beer, wine, liquor, etc. 10. Marijuana, grass, or pot. 11. Other illegal drugs, e.g., crack, cocaine, heroin. 12. Inhalants. 13. Steroids.
232
Appendix C
The drug use composite was created by adding the scores for each of the five items and dividing that score by the number of indicators (i.e., five). The possible values for this variable ranged from 0 to 7. Missing data for this delinquency composite accounted for 6.5% of all cases. After constructing the composite variables for delinquency, aggression, and drug use, the next step was to determine whether these three indicators would load onto the construct of problem behaviors. EFA and reliability analysis using only the three composite variables are presented in Table C7. Factor loadings indicated the presence of a single-factor structure across the four groups. The factor loadings were highest among males and urban respondents. Internal consistency also was highest among male and urban respondents.
Table F7 EFA and Scale Reliability Results for the Problem Behaviors Measure, Across Four Groups Male Female Rural Urban Indicators Delinquency Aggression Drug Use Factor Problem Behaviors
Factor Loadings .859 .788 .695
.798 .665 .803
.845 .787 .752
Total Variance Explained by Factor 74%
71%
67%
75%
.73
.81
α
Scale Problem Behaviors
.846 .632 .674
.80
.77
In general, latent measures seem to have benefited from a reduction in the number of indicators. Factor loadings and total variance explained increased for measures reduced from n > 3 to three
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233
indicators. Reliabilities scores decreased slightly for the Anger Control measure while increasing for the Family Conflict measure. All other reliabilities scores remained stable and at or above acceptable levels. Furthermore, all selected indicators were similar across groups, which will allow for tests of invariance in Chapter 4.
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Index
Abbott, R.D., 2, 3, 4, 244, 246 ACLU, 17, 235 Ageton, S., 5, 58, 62, 235, 239 aggression, 2, 3, 6, 7, 8, 9, 10, 11, 55, 67, 68, 69, 72, 73, 75, 77, 88, 90, 103, 107, 108, 109, 114, 165, 166, 179, 180, 183, 211, 228, 229, 230, 231, 232, 236, 237, 239, 240, 242, 244, 248 Agnew, R., 61, 235 Allport, G.W., 167, 235 Anderson, J., 75, 245 Andreou, E., 106, 235 anger control, 68, 70, 77, 80, 82, 88, 89, 98, 103, 104, 114, 127, 134, 135, 142, 143, 148, 149, 150, 161, 162, 163, 164, 165, 167, 171, 175, 179, 183, 212, 219, 220 Appeal to Higher Loyalties, 61 Armstrong, T., 98, 215, 247 arrestable offenses, 2, 3 Arthur, M.W., 5, 51, 170, 235, 247
Arunkumar, R., 5, 50, 51, 184, 185, 250 Baglioni, J., 170, 235 Bagozzi, R.P., 119, 235 Baltes, P., 51, 249 Barbara, M.B., 235 Baron, R.M., 94, 101, 127, 134, 236 Bartol, A.M.. See Bartol, K.R. Bartol, K.R., 2, 6, 7, 14, 16, 19, 38, 39, 40, 41, 43, 45, 52, 53, 59, 64, 72, 88, 181, 236 Bartollas, C., 2, 13, 14, 15, 16, 17, 40, 52, 53, 54, 59, 61, 62, 236 Bates, J., 72, 109, 247 Bauman, K., 247 Baumeister, R.F., 55, 181, 236, 237 Bearman, P., 247 Becker, H., 5, 57, 236 behavior-system, 7, 49, 161, 164, 167, 186 Benson, J., 118, 236 251
252 Bentler, P.M., 99, 236 Bernard, T., 57, 249 Betts, S., 240 Biron, L., 54, 240 Bischof, G.P., 42, 236 bivariate model, 111, 127, 143 Bloom, B.L., 104, 218, 236 Blum, R., 247 Boden, J.M., 236 Bolger, N., 93, 214, 243 Bollen, K., 99, 112, 201, 236 Bonett, D.G., 99, 236 Boomsma, A., 200, 201, 236 Borduin, C., 70, 250 Botvin, G.J., 70, 165, 242 Braithwaite, H.O., 68, 103, 165, 238 Bronfenbrenner, U., 5, 6, 7, 19, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 51, 52, 64, 78, 90, 92, 164, 167, 178, 181, 182, 186, 236 Bronner, A.F., 242 Brooks-Gunn, J., 237 Brown, B.B., 40, 237 Bryant, F.B., 200, 201, 202, 237 Bu, H., 244 Burgess, E., 76, 246 Bushman, B.J., 55, 181, 236, 237 Byrne, B., 94, 98, 99, 102, 103, 118, 179, 237 Cadenhead, C., 71, 239 Campbell, W.K., 55, 181, 236 Carmines, E.G., 99, 237 Caspi, A., 38, 45, 54, 237, 239, 244, 245 Catalano, R.F., 3, 51, 170, 235, 237, 240, 242, 244
Index CDC, 4, 16, 18, 72, 237, 238, 246 Cernkovich, S.A., 4, 62, 171, 238 CFI, 99, 100, 118, 120, 124, 125, 128, 136, 137, 138, 139, 140, 142, 143, 144, 145, 147, 148, 150, 152, 158 Chambers, J., 76, 246 Chambliss, W.J., 58, 238 Chao, W., 4, 168, 170, 248 Chesney-Lind, M., 4, 72, 73, 74, 75, 114, 164, 166, 183, 238, 243, 248 Christiansen, K.O., 53, 238, 244 chrono, 39 Cloninger, R., 54, 238 Cloward, R., 5, 56, 57, 176, 238 Cohen, A.K., 4, 56, 60, 176, 238 Coles, C.J., 68, 103, 165, 238 Colonial Period, 13 Comrey, A.L., 200, 201, 238 conceptual model, 7, 8, 10, 37, 65, 79, 94, 116 Condemnation of the Condemner, 60 conformity, 56 Conger, J.J., 4, 42, 54, 168, 170, 238, 248 containment theory, 59, 61, 64, 89 Cortes, J.B., 53, 239 Costa, F., 54, 243 Crawford, A., 4, 239 Cressey, D., 53, 59, 249 Crockett, L., 70, 104, 168, 243 Crosnoe, R., 174, 239
Index DeCarlo, L.T., 209, 210, 239 delinquency, 1, 2, 3, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 19, 37, 38, 42, 43, 45, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 86, 88, 89, 90, 92, 93, 103, 104, 105, 106, 107, 114, 164, 165, 166, 174, 180, 183 Delinquency Prevention Act of 1974, 15 Denial of Injury, 60 Denial of Responsibility, 60 Denial of the Victim, 60 Diaz, T., 70, 165, 242 differential association, 59, 60, 61, 62, 64, 87, 88, 89 Dinitz, S., 58, 241 Dishion, T., 244 Doddler, R., 60, 249 Dodge, K., 72, 109, 247 Donnellan, M.B., 55, 239 Downs, W.R., 46, 67, 173, 239 Drapela, L.A., 176, 239 drift theory, 59, 60, 61, 64, 88 Drift theory, 59, 61 Dryfoos, J., 51, 239 Dugdale, R.L., 52, 53, 239 Duncan, G.J., 237 Duncan, S.C., 76, 239 Duncan, T.E., 76, 239 DuRant, R.H., 71, 239 ecological approach, 6, 37 ecological niche, 40 Elder, G.H., 4, 45, 168, 170, 239, 248 Elliot, A., 235
253 Elliott, D.S., 2, 4, 5, 58, 62, 235, 239 Elliott, G.R., 237 EM estimation, 113 Epps, P., 53, 239 Erickson, K.G., 174, 239 Espiritu, R.C., 4, 239 estimation, 92, 98, 101, 102, 111, 112, 113, 115, 116, 129, 143, 178, 203, 204, 206, 207, 208, 209, 211, 215, 236 evaluation, 8, 37, 49, 98, 111, 186, 235 Evans, W., 108, 189, 231, 240 exo, 6, 7, 11, 19, 38, 45, 49, 64, 89, 161, 164, 167 exosystem. See exo Eysenck, H., 52, 53, 54, 88, 240 family conflict, 71, 76, 77, 80, 82, 89, 98, 104, 134, 135, 142, 143, 148, 149, 150, 161, 162, 163, 164, 165, 166, 167, 175, 207, 210, 211, 218 Farrell, A.D., 2, 3, 69, 75, 76, 77, 84, 105, 114, 166, 168, 169, 180, 240 Farrington, D.P., 4, 53, 54, 240, 243, 244, 250 FBI, 2, 17, 24, 72, 240 Feehan, M., 75, 245 Felix-Ortiz, M., 51, 246 female, 12, 66, 67, 73, 74, 76, 77, 93, 104, 106, 111, 113, 114, 116, 119, 126, 129, 134, 135, 142, 143, 148, 149, 154, 162, 163, 164, 168, 169, 170, 171, 173,
254 175, 179, 180, 182, 183, 184, 186, 194, 207, 208, 212, 213, 215, 216, 218, 219, 225, 228, 238 Fergusson, D.M., 71, 240 FIML estimation, 113, 208 Flannery, D.J., 69, 84, 105, 169, 173, 240, 241 Fleming, C.B., 215, 240 Foster, J.D., 58, 241 Fridrich, A.H., 69, 241 gang, 5, 17, 56, 57, 58, 59, 245 Gardner, L., 76, 235, 238, 241, 244 Garmezy, N., 51, 241 Gatti, F.M., 53, 239 Gecas, V., 40, 241 Gibbens, T.C., 53, 241 Gibbs, J.P., 58, 68, 69, 165, 241 Giever, D., 68, 165, 241 Giordano, P.C., 4, 61, 62, 171, 238, 241 Glaser, D.N., 98, 185, 242 Glueck, E.T., 241 Glueck, S., 52, 53, 241 Goddard, H., 52, 53, 241 Gordon, R., 53, 241 Goring, C., 53, 241 Gorman-Smith, D., 2, 70, 104, 241 Gottfredson, D.C., 2, 72, 76, 98, 114, 168, 215, 242, 247 Gottfredson, M.R., 5, 68, 241, 242, 243 Graham, J., 201, 202, 204, 248 Greene, A.F., 68, 103, 165, 238, 241
Index Griffin, K.W., 2, 7, 42, 51, 68, 70, 72, 75, 103, 104, 109, 165, 242 Hammond, P., 57, 246 Harachi, T.W., 215, 240 Harmon, M., 98, 215, 247 Harris, K., 247 Hart, H., 61, 244 Hawkins, D.F., 4, 243 Hawkins, J.D., 2, 3, 5, 51, 170, 235, 237, 242, 244, 246, 247 Hayduk, L.A., 98, 242 Healy, W., 53, 242 Heaven, P., 47, 55, 67, 75, 173, 247 Heimer, K., 60, 62, 245 Henry, D.B., 2, 70, 104, 241 Hepburn, J.R., 58, 242 Herrstein, R., 52, 250 Hill, K., 243, 244, 246, 248, 249 Hillbrand, M., 92, 242 Hindelang, M.J., 52, 53, 61, 242, 243 Hirata, S., 67, 68, 75, 243 Hirschi, T., 5, 8, 52, 53, 56, 59, 61, 62, 68, 71, 88, 106, 107, 171, 172, 176, 241, 242, 243, 249 homogeneity of variancecovariance, 111, 113, 199, 213 homoskedasticity, 112, 211, 212 Horwood, L.J., 71, 240 Huizinga, D., 4, 239 hypothesized model, 10, 11, 37, 66, 74, 76, 77, 78, 86, 89, 97, 102, 113, 143, 151,
Index 154, 162, 165, 181, 184, 185, 186, 200, 213 identification, 51, 68, 91, 95, 96, 97, 111, 129, 140, 239 IFI, 99, 100, 118, 120, 124, 125, 128, 136, 137, 138, 139, 140, 142, 143, 144, 145, 147, 148, 150, 152, 158 In Re Gault, 235 Independence model, 99 infra, 6, 7, 19, 49, 52, 64, 89, 161, 164, 165, 181, 185, 186 infrasystem. See infra innovation, 56, 57 Integrative Model of delinquency, 8, 37, 63, 65 Jaccard, J., 98, 99, 243 Jacobson, K., 70, 104, 168, 243 Jenkins, P., 172, 243 Jensen, G., 58, 59, 62, 243 Jessor, R., 2, 3, 5, 6, 7, 19, 45, 46, 47, 48, 49, 52, 54, 64, 66, 75, 88, 164, 167, 178, 181, 182, 243 Jessor, S.. See Jessor, R. Johnsen, M., 250 Jones, J., 247 Juvenile Court Period, 14 Juvenile Rights Period, 15 Kashy, D., 93, 214, 243 Kelly, J., 69, 71, 107, 227, 241, 246 Kempf-Leonard, K., 4, 243 Kenny, D.A., 93, 94, 101, 127, 134, 214, 236, 243 Kirkegaard-Sorenson, L., 244 Klebanov, P.K., 237 Klein, D., 73, 75, 164, 183, 244
255 Koper, C.S., 2, 72, 242 Kornhouser, R., 57, 244 Kosterman, R., 3, 237 Krohn, M., 4, 244 Krueger, J.I., 54, 55, 236, 237 Kung, E.M., 2, 3, 69, 84, 105, 240 labeling, 57, 58 Lambert, S., 4, 240 Landsheer, J., 61, 244 latent variable model, 94, 97, 101, 111, 117, 134, 135, 140, 141, 142, 143, 145, 150, 151, 174, 177, 201, 214 Laub, J., 4, 248 Laurie, B., 69, 71, 107, 227, 246 Le Blanc, M., 4, 244 LeBlanc, M., 54, 240 Lee, H.B., 200, 201, 238 Leiter, J., 250 Lemert, E., 57, 244 Lewin, K., 40, 41, 48, 49, 244 limitations, 7, 12, 97, 175, 204 Lind, J., 99, 249 Linder, C.W., 239 Lipsitt, P., 244 Little, R., 111, 177, 208, 244 locus of control, 7, 46, 55, 66, 67, 75, 77, 81, 84, 85, 88, 89, 98, 105, 106, 114, 119, 126, 127, 134, 135, 142, 143, 148, 149, 154, 161, 167, 170, 171, 172, 173, 174, 178, 182, 185, 207, 225, 246 Loeber, R., 2, 4, 38, 70, 75, 104, 239, 241, 243, 244, 245, 249 Lomax, R., 98, 99, 248
256 Lombroso-Guerrero, G., 52, 244 Lonczak, H.S., 2, 7, 244 Lynam, D., 38, 54, 245, 246 Lynskey, M.T., 71, 240 Lytton, H., 4, 245 MacCallum, R., 99, 245 macro, 6, 7, 19, 44, 45, 49, 59, 64, 74, 75, 89, 161, 164, 166, 183, 186 macrosystem. See macro male, 12, 66, 67, 72, 74, 75, 77, 93, 104, 106, 111, 113, 116, 119, 126, 129, 134, 135, 142, 148, 154, 162, 163, 164, 165, 169, 171, 180, 182, 184, 207, 208, 212, 213, 215, 216, 218, 219, 225, 228, 232, 238, 244 Marsh, H., 96, 245 Marsiske, M., 51, 249 Marte, R.M., 240 Martin, J.S., 68, 165, 241 Maruyama, G., 98, 99, 102, 245 Masten, A., 50, 51, 184, 241 Matsueda, R., 60, 62, 245 Matza, D., 5, 59, 60, 61, 64, 88, 245, 249 MCAR, 111, 113, 177, 202, 203, 204, 206, 207, 208 McCandless, B., 53, 245 McGee, R., 75, 245 McIver, S.P., 99, 237 McKay, H., 55, 56, 64, 76, 87, 248 Measurement Intercepts model, 119 measurement model, 94, 95, 96, 97, 103, 105, 111, 116,
Index 117, 118, 119, 124, 126, 166, 179, 214, 215, 216 Measurement Residuals, 119, 124, 125, 126, 128, 129, 141, 147, 152 Measurement Weights model, 119, 124 Mednick, S., 53, 238, 244 Meece, D., 72, 109, 247 Merton, R., 5, 56, 57, 64, 90, 176, 245 meso, 6, 7, 19, 38, 43, 45, 49, 64, 89, 161, 164, 167, 186 mesosystem. See meso micro, 6, 7, 19, 38, 42, 43, 45, 64, 89, 161, 164, 175, 186 microsystem. See micro Miller, J., 245 Miller, J.Y., 242 Miller, N., 70, 165, 242 Miller, N.L., 242 Miller, W.B., 245 Miller, W.C., 51, 54, 56, 238, 240 Millsap, R., 99, 117, 246 Mitchell, J., 60, 249 MMWR, 17, 246 Moffit, T., 38, 54, 245, 246 Moffitt, T., 4, 54, 237, 239, 244, 246 Morrison, G., 69, 71, 107, 227, 246 Mulaik, S., 99, 117, 246 multicollinearity, 111, 113, 199, 213 multi-group invariance, 94, 112 Muuss, R., 6, 37, 39, 40, 41, 42, 43, 44, 45, 246 Myers, K., 250
Index neighborhood risk, 72, 77, 89, 109, 127, 134, 135, 143, 150, 161, 163, 164, 166, 169, 170, 182, 185 Nettler, G., 57, 58, 246 neutralization, 59, 60, 61, 235 Newcomb, M.D., 3, 51, 237, 246 NFI, 99, 100, 118, 120, 124, 125, 128, 136, 137, 138, 139, 140, 142, 144, 145, 147, 150, 151, 152, 156, 158 Novak, S., 38, 245 Nowicki, S., 106, 225, 246 Nunnally, J., 118, 214, 246 O’Donnell, J., 2, 3, 51, 246 Ohlin, L., 5, 56, 57, 176, 238 OJJDP, 3, 15, 246 Osgood, D., 76, 246 Oxford, M.L., 215, 239, 240, 241, 248, 249, 250 Palmore, E., 57, 246 Paramore, V.V., 72, 73, 238 parens patriae, 14, 17 parental monitoring, 4, 5, 41, 42, 60, 68, 69, 77, 80, 82, 87, 88, 89, 98, 104, 105, 114, 142, 143, 148, 149, 150, 153, 161, 162, 163, 165, 166, 167, 168, 169, 172, 173, 174, 175, 179, 182, 185, 186, 210, 211, 216, 221, 223, 247 Park, R., 76, 239, 246 Parnell, R., 53, 239 Parrott, C., 46, 67, 75, 106, 246 Passingham, R., 54, 247 PBT. See problem behavior theory
257 Pedhauzer, E., 118, 247 peer, 4, 5, 10, 42, 43, 46, 47, 48, 61, 62, 64, 68, 69, 77, 80, 81, 83, 84, 85, 88, 89, 91, 105, 114, 119, 127, 129, 134, 135, 142, 143, 149, 150, 153, 154, 161, 169, 170, 171, 172, 173, 174, 175, 178, 184, 185, 189, 210, 211, 216, 222, 223, 237, 239, 242, 247, 249 peer influence, 47, 61, 64, 69, 77, 81, 84, 85, 86, 89, 114, 127, 143, 149, 153, 169, 170, 171, 172, 173, 174, 175, 178, 216 peer pressure, 5, 69, 80, 81, 88, 105 Peiser, N., 47, 55, 67, 75, 173, 247 Pendergrast, R.A., 71, 239 Personal theories, 52 Persons, W., 17, 53, 245 Pettit, G., 72, 109, 247 Pfefferbaum, B., 69, 165, 247 Platt, A., 13, 75, 247 Pleydon, A., 69, 84, 105, 169, 247 Pollard, J.A., 5, 51, 170, 235, 247 problem behavior theory, 3, 7, 19, 45, 46, 90, 178, 181 protective, 5, 6, 7, 10, 11, 19, 50, 51, 75, 89, 101, 142, 148, 161, 162, 166, 173, 178, 184, 185, 186, 189, 197, 241, 242, 246 Rankin, J., 62, 250 rebellion, 57
258 Reckless, W.C., 5, 58, 59, 61, 62, 64, 88, 89, 241, 247 Reform Agenda, 15 resiliency, 5, 12, 50, 51, 90, 184, 185, 187 Resnick, M., 106, 107, 108, 227, 230, 231, 247 retreatism, 57 Richardson, G., 5, 247 risk, 1, 4, 5, 6, 7, 10, 11, 13, 16, 19, 50, 51, 62, 67, 68, 70, 71, 72, 75, 77, 78, 80, 81, 82, 83, 84, 87, 89, 92, 101, 109, 114, 119, 127, 129, 134, 135, 142, 143, 150, 153, 161, 162, 163, 164, 166, 167, 168, 169, 170, 171, 174, 176, 178, 180, 182, 184, 185, 186, 189, 202, 209, 237, 238, 239, 241, 242, 244, 246, 250 risk factor, 5, 11, 52, 161, 185 ritualism, 56 Rivera, C., 4, 57, 244, 248 RMSEA, 99, 100, 118, 120, 125, 126, 128, 136, 137, 138, 139, 140, 142, 143, 144, 145, 147, 148, 150, 152, 154, 156, 158 Roberts, A., 53, 245 Robertson, L., 69, 71, 107, 227, 246 Robins, R.W., 54, 239 Rosay, A., 98, 99, 117, 118, 215, 247 Rose, S.R., 46, 67, 173, 239 Rotter, J., 66, 105, 225, 247 rural, 5, 12, 69, 76, 77, 93, 104, 106, 111, 113, 114, 116, 126, 127, 129, 135,
Index 148, 149, 150, 151, 154, 156, 162, 163, 164, 168, 169, 171, 175, 179, 180, 182, 183, 184, 200, 201, 207, 208, 212, 213, 215, 216, 218, 219, 228 Rutter, M., 51, 248 Sadowski, C., 67, 248 Sampson, R., 4, 248 Sanders, W., 13, 248 Saturated model, 123 Scaramella, L., 42, 248 Schafer, J., 201, 202, 248 Scheier, L., 70, 165, 242 Schmelkin, L., 118, 247 Schmutte, P., 54, 237 Schner, J., 69, 84, 105, 169, 247 Schumacker, R., 98, 99, 248 Schwarzer, R., 54, 249 Sealand, N., 237 Sealock, M.D., 2, 72, 242 Seff, M., 40, 241 Selvin, H., 8, 243 Shaw, C., 5, 47, 55, 56, 64, 67, 76, 87, 248 Shek, D., 70, 104, 248 Shelden, R., 73, 248 Shoemaker, D.J., 4, 5, 8, 37, 52, 58, 59, 62, 63, 64, 65, 76, 84, 86, 90, 170, 241, 248, 250 Short, J., 57, 248 Sickmund, M., 1, 16, 18, 249 Silliman, B., 240 Silva, P., 54, 237, 246 Simons, R., 4, 42, 168, 170, 173, 175, 248 Singh, B.B., 72, 164, 250 Singh, B.K., 72, 164, 250
Index Slavens, G., 71, 239 Slawson, J., 53, 249 Smart, L., 55, 181, 236 Smith, R., 51, 241, 250 Snyder, E., 58, 249 Snyder, H.N., 1, 16, 18 social bond, 62, 71, 77, 81, 84, 85, 86, 88, 89, 98, 106, 107, 114, 127, 135, 142, 143, 149, 150, 153, 154, 161, 171, 174, 176, 179, 185, 210, 211, 212, 226, 227, 228, 243 Social Control, 16, 239 social control theory, 59, 60, 61, 62, 88, 106, 171, 176 Social disorganization theory, 55 Social process theories, 59 Social reaction theories, 57 Social Structural theories, 55 social systems framework, 1, 3, 5, 6, 7, 8, 12, 37, 38, 161, 164, 181, 185, 186 Souma, I., 68, 75, 243 specification, 94, 95, 96, 97, 111, 116, 127, 140 Spergel, I., 57, 249 Spielberger, C., 103, 217, 249 Spoth, R., 42, 248 Staudinger, U., 249 Steiger, J., 99, 249 Steinberg, L., 40, 168, 249 Stith, S.M., 42, 236 Stouthamer-Loeber, M., 4, 54, 237, 244, 249 strain theory, 56, 64, 176 Strickland, B., 106, 225, 246 Strongman, K., 46, 67, 75, 106, 246
259 Structural Covariances model, 119 Structural Means model, 119, 127 Strycker, L.A., 76, 239 subculture, 55, 57 Sullivan, J., 17, 249 Sutherland, E., 5, 53, 59, 60, 62, 64, 87, 88, 249 Sykes, G., 59, 60, 88, 249 systems theory, 6, 7, 8, 11, 19, 37, 45, 47, 48, 49, 50, 52, 64, 65, 90, 164 systems theory framework, 6, 7, 8, 11, 19, 37, 49, 52, 64 Takanishi, R., 51, 250 Tang, C., 54, 249 Tellegen, A., 51, 241 The Houses of Refuge Period, 13 Thompson, W., 60, 62, 249 Thornberry, T., 4, 5, 244, 249 Tolan, P., 2, 70, 104, 241 Trzesniewski, K.H., 239 Unconstrained model, 119, 124, 127, 141, 145, 146, 150, 151 urban, 5, 12, 55, 69, 72, 76, 77, 93, 104, 106, 111, 113, 114, 116, 126, 127, 129, 135, 148, 149, 150, 151, 153, 154, 156, 162, 163, 164, 165, 166, 168, 169, 170, 171, 180, 182, 183, 184, 200, 201, 207, 208, 212, 213, 215, 216, 218, 219, 225, 228, 232, 239, 241, 242, 248 Valois, R.F., 2, 3, 69, 84, 105, 240
260 Van Den Bos, J., 54, 243 Vanderryn, J., 54, 243 variance-covariance, 97, 98, 112, 117, 127, 212, 213 Vazsonyi, A.T., 69, 84, 105, 169, 240 Vohs, K.D., 55, 236 Vold, G., 57, 249 Wadsworth, M., 53, 250 Wan, C., 98, 100, 243 Watanabe, T., 68, 75, 243 Weis, J.G., 5, 242 Wells, L., 62, 250 Wenzel, D., 67, 248 Werner, E., 51, 250 West, D.J., 4, 53, 240, 250 White, S.O., 2, 3, 69, 84, 105, 240 Wikstroem, P., 38, 245 Williams, J.S., 72, 164, 250
Index Williams, L.L., 69, 84, 105, 169, 240 Williams, R.A., 70, 250 Williams, S., 75, 245 Wilson, J., 52, 250 Wolfe, T., 4, 250 Wong, C., 54, 249 Wood, P., 69, 165, 247 Yarnold, P.R., 200, 201, 202, 237 Yi, Y., 119, 235 Youth Online: Comprehensive Results, 1, 18, 250 Zaslow, M., 51, 250 Zeller, R.A., 118, 237 Zimmerman, M., 5, 50, 51, 184, 185, 250 Zingraff, M., 62, 171, 250 Zuckerman, M., 54, 250
