DELINQUENT PEERS, BELIEFS, AND DELINQUENT BEHAVIOR: A LONGITUDINAL TEST OF INTERACTIONAL THEORY

DELINQUENT PEERS, BELIEFS, AND DELINQUENT BEHAVIOR: A LONGITUDINAL TEST OF INTERACTIONAL THEORY*

TERENCE P. THORNBERRY ALAN J. LIZOTTE MARVIN D. KROHN

MARGARET FARNWORTH

SUNG JOON JANG

The University at Albany, State University of New York

Sam Houston State University

The Ohio State University

Three theoretical models of the interrelations among associations with delinquent peers, delinquent beliefs, and delinquent behavior are examined. The socialization model views delinquent peers and beliefs as causally prior to delinquent behavior, whereas the selection model hypothesizes that associations with delinquent peers and delinquent beliefs are a result of delinquent behavior. The interactional model combines aspects of both the socialization and the selection models, positing that these variables have bidirectional causal influences on one another over time. Data to test for reciprocal causality are drawn fiom three waves of the Rochester Youth Development Study. Results suggest that simple unidirectional models are inadequate. Associating with delinquent peers leads to increases in delinquency via the reinforcing environment of the peer network. Engaging in delinquency, in turn, leads to increases in associations with delinquent peers. Finally, delinquent beliefs exert lagged effects on peers and behavior, which tend in turn to “harden” the formation of delinquent beliefs.

Social scientists generally agree that substantial consistency exists between a person’s behavior and immediate social environment. For

* The authors would like to thank David McDowall of the University of Maryland, Leslie Hayduk of the University of Alberta, Kenneth Land of Duke University, Richard McCleary of the University of California at Irvine, and Allen Liska of the University at Albany for reading earlier drafts of this paper. This study was prepared under Grant 86-JN-CX-0007 (S-3) from the Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, U.S. Department of Justice, Grant 5 R01 DAO5512-02 from the National Institute on Drug Abuse, and Grant SES-8912274 from the National Science Foundation. Points of view or opinions in this document are those of the authors and do not necessarily represent the official position or policies of the funding agencies.

CRIMINOLOGY VOLUME 32 NUMBER 1 1994 47

THORNBERRY ET AL.

example, individuals tend to construct social worlds in which their behaviors, belief systems, and friendship networks are consonant with one another. Although social scientists generally agree on this observation, they differ dramatically as to the causes of such consistency and have offered three general perspectives to account for it. A socialization perspective argues that social forces such as beliefs and peer influences cause behavior, thereby generating consistency. A social selection perspective argues that behavior patterns cause the formation of consistent belief systems and the selection of similar peers. An interactional perspective argues that variables of this sort influence each other reciprocally over the life course to generate increasing consistency. All three perspectives therefore offer explanations-albeit competing ones-for the generally observed consistency between the individual and his or her social environment.

Resolving which perspective is most accurate is an important theoretical and empirical issue because at stake is an understanding of a basic process concerning how human behavior develops and how it is related to other social forces. Do social forces such as attitudes and associational patterns shape behavior, does behavior shape the formation of attitudes and associational patterns, or do these social forces and behaviors influence each other over the life course? This basic question has been raised in a number of areas of inquiry including religiosity, prejudice and discrimina- tion, mental illness, drug use, and delinquency. Resolution of this question in any one area is important because that may inform its resolution in other areas and therefore its resolution more generally. The present study examines this issue with respect to the development of one form of behavior-juvenile delinquency.

EXPLANATIONS FOR DELINQUENCY

It is widely agreed that both delinquent peers and delinquent beliefs are correlated strongly with delinquent behavior, thereby creating a consistent social environment for most delinquents. Although the strength of the correlations between delinquent peers and behavior (e.g., Elliott et al., 1985a; Johnson, 1979) and between delinquent beliefs and behavior (e.g., Matsueda, 1982) appears to be indisputable, there is little agreement on how these variables are interrelated theoretically or causally. That is, scholars do not agree on how to weave these empirical observations into a set of logically interrelated propositions that offer a coherent explanation for delinquency. Indeed, each of the three general theoretical perspectives mentioned above-the socialization, the selection, and the interactional perspective-has been used to account for these relationships.

PEERS, BELIEFS, AND DELINQUENCY 49

The socialization perspective stems from the differential association theory tradition of criminology and grants causal priority to associations with delinquent peers (Sutherland and Cressey, 1978). Peer affiliations provide the environment for the learning and reinforcement of both beliefs and behavior; affiliations therefore are a major cause of both outcomes. Ado- lescents who associate with conforming peers are apt to be conforming in beliefs and actions; adolescents who associate with delinquent peers are apt to be delinquent in beliefs and actions.

In some versions of the socialization perspective, peer associations have only an indirect effect on behavior, via beliefs (see, for example, Mat- sueda, 1982; Reed and Rose, 1991). In other versions, peer associations have a direct effect on behavior as well (Warr and Stafford, 1991). The second model, with both direct and indirect effects, is the one found most commonly in integrated theories of delinquency (e.g., Elliott et al., 1985a; Johnson, 1979).

The selection perspective, which is derived primarily from social control theory (Glueck and Glueck, 1950; Hirschi, 1969), reverses the causal relationships among these variables. Arguing that “birds of a feather flock together,” this perspective suggests that adolescents who already are delinquent seek each other out for companionship. Delinquency is caused by a weakening of social controls; once exhibited, one of its effects is to increase associations with delinquent peers. (See Liska, 1969, 1973 for a general discussion of the selection perspective.)

An interactional perspective (Thornberry, 1987) combines aspects of both the socialization and the selection perspectives. Rather than assigning unidirectional causal priority to one of these variables over the others, it argues that they are likely to be interrelated over the life course and to have bidirectional causal influences on one another. For example, delinquent peers are likely to reinforce delinquency, and as the subject anticipates and experiences those positive peer reactions, delinquency is likely to increase. In turn, the more a person engages in delinquency, the more likely he or she is to associate with delinquent peers. Therefore, these variables become interrelated over time.

In sum, the socialization perspective grants causal priority to peer associations, the selection perspective grants priority to delinquent behavior, and the interactional perspective accords each concept a substantial causal role with respect to the other. This article examines empirical support for these different causal structures, with particular attention to the claim of interactional theory that strong bidirectional relationships exist between these variables.

THORNBERRY ET AL.

INTERACTIONAL THEORY

Interactional theory builds on the recognition that an adequate explanation of delinquency cannot be limited to either a social control or a social learning perspective, but should incorporate successful elements of each into a broader body of explanatory principles. Although interactional theory is similar to these theories and to some integrated approaches (e.g., Elliott et al., 1985a; Hawkins and Weis, 1985; Johnson, 1979) in terms of the primary concepts contained in the model, it attempts to go beyond them by responding to three general limitations of theories of delinquency and crime. Most theories fail to pay sufficient attention to the impact of social structure on behavior, tend to be nondevelopmental, and ignore reciprocal causal relationships. The present research focuses largely on how interactional theory addresses the third issue, although the theoretical model is informed by the developmental argument of interactional theory.

From an interactional perspective, the major shortcoming of traditional theories of delinquency concerns their static, unidirectional causal structure. Both differential association and social control theories have this structure, although they differ in theoretical specification and the ordering of the variables within the structure. Some derivations of these traditional theories have recognized the need to include bidirectional relationships (e.g., social learning theory), but the theoretical specification and empirical tests of these arguments tend to revert to unidirectional examinations.

Interactional theory elaborates on these models in trying to form a more comprehensive explanation of delinquency. The causal structure of interactional theory accordingly differs from preceding theories and offers different hypotheses about how delinquency and its possible causes are interrelated.

MODEL SPECIFICATION

Interactional theory asserts that the fundamental cause of delinquency is the weakening of a person’s bond to conventional society. Adolescents who are attached strongly to their parents and family, are committed deeply to conventional institutions, and believe firmly in conventional values are unlikely to engage in serious and repetitive acts of delinquency. Adolescents who are not bonded to conventional society in this manner are far more likely to be delinquent. If the freedom associated with weak or absent social controls is to lead to serious and prolonged delinquency, however, an environment is required in which delinquency can be learned and reinforced. That environment is provided largely by associations with delinquent peers and by the formation of delinquent beliefs. In other


words, associations and beliefs represent the more proximal causal relationships with delinquency.1

Figure 1 shows the specification of interactional theory for the causal relationships among these variables. This figure presents a three-wave panel model covering the period of transition from early adolescence to the beginning of middle adolescence.* This is the time span covered by the data to be used in estimation. The specification of this model can be discussed in terms of two theoretical issues. The first concerns the relationship between peers and behavior; the second concerns delinquent beliefs.3

PEERS AND BEHAVIOR

Three variables are included in the peer-behavior relationship. Delin- quent Peers measures the extent to which the subject associates with peers who are delinquent; Peer Reactions measures the subject’s perception of whether peers will react positively or negatively to the subject’s own delinquency; Delinquent Behavior measures the subject’s delinquency.

At a general level, interactional theory argues that adolescents who associate with delinquent peers are likely to commit delinquent acts, and that those who commit delinquent acts are likely to continue associating with delinquent peers. That is, these variables reinforce each other to create a behavioral trajectory toward increasing levels of delinquency and greater entrenchment in delinquent peer networks. In Figure 1 this idea is represented most clearly by the contemporaneous causal loops among Delinquent Peers, Peer Reactions, and Delinquent Behavior at ‘limes 2 and 3, and to a somewhat lesser extent by the lagged relationships among these variables. Because the causal loop represents the crux of our argument, we focus on it in this discussion.

The first hypothesis, that associating with delinquent peers causes delinquent behavior, is derived from social learning theory, especially the work of Akers (1977) and Akers et al. (1979). Delinquent peer networks provide a social environment in which delinquency is reinforced; because of

1. An earlier paper (Thornberry et al.. 1991) examined reciprocal relationships among delinquency, attachment to parents, and commitment to school. It found that commitment to school and delinquency are related reciprocally. Attachment to parents and delinquency are related reciprocally in earlier adolescence; later there is a negative unidirectional effect from delinquency to attachment to parents. These results are consistent with the developmental hypotheses of interactional theory.

2. The mean ages of respondents at the three time periods covered here are 13.9, 14.4, and 14.9.

3. Other aspects of the specification, including the paths from the demographic variables, are discussed in the section on methods.

52 THORNBERRY ET AL.


that reinforcement, members of the network are likely to engage in delinquent behavior.

Because social reinforcements are expected to mediate the effect of differential associations on delinquent behavior, a two-step path is included in Figure 1 at both Time 2 and Time 3, The first path leads from Delin- quent Peers to Peer Reactions; subjects who are members of heavily delinquent peer networks are likely to perceive that members of those networks will reinforce delinquent behavior. The second path leads from Peer Reactions to Delinquent Behavior; the more one anticipates and receives positive reinforcements for delinquent behavior, the more likely one is to engage in that behavior. Therefore associating with delinquent peers generates delinquent behavior indirectly via the reinforcing environment of the delinquent peer group. Buehler et al. (1966) report observa- tional data supporting this specification. Similarly, Agnew, in his examination of interaction effects involving peer approval (or reinforcement), peer attachment, and time spent with peers, reports that “Peer Approval for Delinquency is the most important of these variables, especially in the equation for Serious Delinquency” (1991a:65).4

The reciprocal hypothesis, that engaging in delinquency exerts a causal impact on associations, is represented by a direct path from Delinquent Behavior to Delinquent Peers. People who engage in delinquency are apt to seek out, or be forced into, associational patterns with others who engage in delinquency. It is implausible that, on average, serious delinquents will associate with “saints” or that “saints” will associate with serious delinquents. Thus interactional theory argues that associations with delinquent peers

and delinquent behavior are mutually related and that unidirectional spec- ifications are simplistic and mis1eading.s Over time, the more one associates with delinquent peers, the more likely one is to be reinforced for and

4. In addition to its theoretical importance, there is a distinct methodological advantage to allowing Peer Reactions to intervene in the causal effect of Delinquent Peers on Delinquent Behavior. When repeated measures are used in panel data, the specification of causal loops without intervening variables generally results in severe collinearity problems. Furthermore, if theoretically important cross-lagged effects also are included, the model may have too few instruments to make estimation possible. Both problems are solved by the inclusion of a theoretically relevant intervening variable in part of the loop.

5. Agnew also argues that “research on delinquent peers . . . has been rather simplistic” (1991a:47). Rather than examining reciprocal causal influences involving delinquent peers over time, however, he examines multiple dimensions of peer relationships and how these dimensions interact to produce delinquent behavior. His results suggest the presence of interaction effects, especially for more serious forms of delinquency. Thus both his research and the present study suggest that the relationship between peer associations and delinquency is more complex than typically portrayed in


to engage in delinquency. In turn, the more one engages in delinquency, the more apt one is to associate with delinquent peers.

DELINQUENT BELIEFS

The role of Delinquent Beliefs in interactional theory varies developmentally. At early adolescence, Delinquent Beliefs is viewed primarily as a consequence of associations and behavior and is expected to have only a weak (if any) causal influence on these variables (see Thornberry, 1987:872-873 for the rationale for this specification). At middle adolescence, however, Delinquent Beliefs is viewed as related reciprocally to these other variables (Thornberry, 1987:879). Because the model under investigation here covers the transition period from early to middle adolescence, Figure 1 specifies lagged effects from Delinquent Beliefs to Delinquent Peers and to Delinquent Behavior, as well as lagged and contemporaneous effects from Delinquent Peers and Delinquent Behavior to Delinquent Beliefs. This specification allows for an assessment of lagged reciprocal relationships among these variables and provides an opportu- nity to examine empirical evidence on an important debate in criminological theory.

The debate concerns the role of delinquent beliefs in the etiology of delinquency. One argument holds that although weakened bonds to conventional society may increase delinquent behavior and delinquent associations, they are unlikely to lead directly to the formation of prodelinquent beliefs. Simply being bonded poorly to society should not produce the belief that it is acceptable to be delinquent unless the individual engages in delinquency, associates with delinquent peers, or both. Engag- ing in delinquent behavior is likely to lead to the formation of delinquent beliefs because of cognitive consistency: “To my mind, the assumption that delinquent acts come before justifying beliefs is the more plausible causal ordering. . . . It is in fact in many cases difficult to imagine how the boy could subscribe to the belief without having engaged in the delinquent acts” (Hirschi, 1969:208).6 This is essentially the argument offered by interactional theory for early adolescence.

On the other hand, differential association theory views the adoption of delinquent beliefs as causally antecedent to delinquent behavior. In the most common formulation of this argument, delinquent beliefs mediate the relationship between delinquent peers and delinquent behavior. Warr

the literature. An intriguing, but challenging, next step might be the simultaneous examination of interaction and reciprocal effects.

6. Hirschi also acknowledges that at a later point in the delinquent career these beliefs become causes of future delinquent acts; this position is consistent with the developmental perspective of interactional theory.


and Stafford (1991) recently argued that according to differential association theory, respondents’ beliefs also can mediate the effect of friends’ delinquent attitudes on respondents’ delinquent behavior. The fact that the sample in this study overrepresents high-risk youths might suggest that such exposure is likely to take place, and therefore that delinquent beliefs might precede delinquent peers and delinquent behavior in the causal chain. The inclusion of lagged relationships among these variables, and the indirect paths that these relationships imply, allow for an examination of these issues.

SUMMARY

Interactional theory views the relationships among delinquent peers, delinquent beliefs, and delinquent behavior more comprehensively than do control, differential association, and integrated theories. Combining the strengths of these theories, it argues that the causal effects of these variables are developmentally specific and, especially by middle adolescence, reciprocally related. In the remainder of this article we examine this claim empirically, first by reviewing the relevant literature and then by empirically estimating the model presented in Figure 1.

PRIOR RESEARCH Previous studies show consistently that the variables under study here

are correlated with one another. Kornhauser (1978) characterizes the relationship between associations with delinquent peers and delinquency as the strongest, most consistent relationship in the research literature. Both cross-sectional (e.g., Agnew, 1991a; Akers et al., 1979; Johnson, 1979; Krohn, 1974; Short, 1957; Voss, 1964) and longitudinal studies (e.g., Elliott et al., 1985a; Krohn et al., 1985; Patterson and Dishion, 1985; Warr and Stafford, 1991) have established that juveniles who associate with delinquent peers are more likely to engage in delinquency than juveniles who associate with nondelinquent peers. The relationship between deviant beliefs and delinquency is less strong and less consistent than that between peers and delinquency, in part because of the variety of ways in which beliefs have been measured. Nevertheless, both cross-sectional and longitudinal studies report that having attitudes which support deviant behavior, or at least support such behavior under certain conditions (techniques of neutralization), is related to the probability of committing deviant behavior (Akers et al., 1979; Jensen, 1972; Matsueda, 1982; Minor, 1981, 1984; little et al., 1986; Warr and Stafford, 1991). The link between associating with delinquent peers and holding delinquent beliefs has not been the focus of many studies, but those which have examined this issue find significant positive relationships (Akers et al., 1979; Elliott et al., 1989;


Johnson, 1979; Matuseda, 1982; Warr and Stafford, 1991). Finally, although few studies incorporate a measure of peer reactions, those which do so have found that peer reactions are related both to associations with deviant peers and to deviant behavior (Agnew, 1991a; Akers et al., 1979; Krohn et al., 1985; White et al., 1987). Warr and Stafford (1991) found that respondents’ beliefs did not mediate the relationship between friends’ behavior and delinquency, and that friends’ attitudes were at best weakly related to delinquent behavior.

At issue, then, is not whether these variables are related, but whether the causal links among them are consistent with a socialization, a selection, or an interactional specification. To distinguish among these specifica- tions, empirical studies that allow for bidirectional relationships are needed. Relatively few studies have examined nonrecursive relationships for these variables;’ those which do so are reviewed here for each of the bivariate relationships.8

PEERS AND DELINQUENCY

Interactional theory argues that a strong causal loop exists between delinquency and associations with delinquent peers. In a study of high school students’ marijuana use, Kandel (1978) reports that selection and socialization effects are of approximately equal importance. Her data suggest a reciprocal relationship between these variables because “adolescents who share certain prior attributes in common tend to associate with each other and tend to influence each other as the result of continued association” (Kandel, 1978:435). Kandel’s study, based on data from best- friend dyads, is quite important because it tends to validate the other studies of peer associations, which rely on the focal subject’s reports of peer delinquency and drug use.

Elliott and Menard (in press) examine the causal order between delinquent peers and delinquent behavior based on the first six waves of the National Youth Survey (NYS).9 They report:

7. This scarcity is due in part to the relative recency of dynamic theoretical perspectives and in part to the empirical requirements for testing reciprocal models, which require sophisticated statistical routines and panel data based on relatively large samples. Instrumental variables also can be used to identify these models with cross-sectional data, but that option has not been used frequently in criminological research. Furthermore, cross-sectional models are misspecified by design when they exclude measures at previous time points and stability effects that would be statistically significant.

8. Thornberry (in press) presents a more thorough review of the literature testing reciprocal relationships that involve delinquency. That review discusses in much greater detail each of the studies reviewed here.

9. The NYS sample covered 11- to 17-year-olds at the first data collection point. Most of the other studies reviewed here are based on samples of high school students;


The typical progression for those who are non-delinquent and in nondelinquent peer groups is (1) movement into a slightly more delinquent peer group, (2) onset of Minor delinquency, (3) movement into a more delinquent peer group, (4) onset of Index delinquency, and (5) movement into a' predominantly delinquent peer group.

The influence of peer associations on behavior appears to be more pro- nounced at initiation. After that point the relationship becomes reciprocal; each variable tends to amplify the other as the delinquent career is maintained.

Studies that include other variables, derived most often from a social control perspective, also find a strong reciprocal relationship between delinquent associations and delinquent behavior. Using National Youth Survey (NYS) data, Agnew (1991b) and Elliott et al. (1985b) report that delinquent peers and delinquent behavior are involved in a strong causal loop. Reed and Rose (1991), using the same data set, report a stronger effect from delinquent behavior to delinquent peers than from delinquent peers to delinquent behavior. Other studies reporting a reciprocal relationship include Burkett and Warren (1987), Ginsberg and Greenley (1978), Meier et al. (1984), and Paternoster (1988).

BELIEFS AND DELINQUENCY

Prior studies of the relationship between delinquent behavior and delinquent beliefs are based on high school-age or older samples. At these ages the relationship between these variables is expected to be bidirectional. None of these studies, however, test the hypothesis of interactional theory that at early adolescence Delinquent Behavior has a unidirectional effect on Delinquent Beliefs.

Results of prior studies generally support the hypothesis of interactional theory that a reciprocal relationship exists at later ages. For example, Agnew (1985), Matsueda (1989), Menard and Huizinga (1990), Minor (1984), and Paternoster (1988) all report reciprocal relationships, but generally with rather small effects. On the other hand, Agnew (1991b) and Elliott et al. (1985b) report that when other variables are included in the model, delinquent beliefs and delinquent behavior are not related directly; in other words, they find neither unidirectional nor bidirectional effects.

the exceptions are the studies by Ginsberg and Greenley (1978) and Minor (1984), which use college students. Descriptive information on these studies is presented in Thornberry's review of the literature (in press:Table 1).


PEERS AND BELIEFS

The final bivariate relationship concerns delinquent beliefs and associations with delinquent peers. Only three studies have examined this association. Agnew (1991b), analyzing the first two waves of the NYS, reports a lagged effect from delinquent beliefs to peer associations but no effect from associations to beliefs. Paternoster (1988) reports a weak and inconsistent effect from peer associations to delinquent beliefs.10 Elliott et al. (1985b), using the first six waves of the NYS, report significant effects both from beliefs to associations and from associations to beliefs.

SUMMARY

Results of prior research provide little support for either unidirectional causal perspective-socialization or selection. Empirical studies that allow for reciprocal relationships, however, find consistent evidence for such relationships. This result is clearest in the case of delinquent peers and delinquent behavior, the most frequently studied relationship. It is also seen for the relationships between beliefs and peer associations and between beliefs and delinquent behavior. Therefore, with respect to the causal relationships among these three variables, prior research is most consistent with an interactional perspective. In the remainder of this article we continue to examine this perspective by testing the theoretical model presented in Figure 1.

METHODS The data for this analysis are drawn from the Rochester Youth Develop-

ment Study (RYDS), a multiwave panel study designed to examine the development of delinquent behavior and drug use among adolescents. Each sample member and the adult primarily responsible for his or her carell are interviewed at six-month intervals. Data also are collected from the Rochester schools, police, and other agencies that have contact with youths. Although the RYDS uses this broad-based data collection strategy, the present analysis is based on information collected in interviews with adolescents at Waves 2, 3, and 4.12 At Wave 2 the students were in

Paternoster’s model specification does not make clear whether the effect from beliefs to peer associations is not significant or is not estimated.

In 85% of the cases the primary caretaker was the natural mother, in 6% it was the natural father, and in the remaining 9% it was another adult.

To maintain consistency in measurement, we use Waves 2-4 rather than Waves 1-3 because we changed our way of measuring peer delinquency and peer reactions between Waves 1 and 2 to accommodate the inclusion of social network measures in the study. Also, in this analysis, we use Wave 1 parent interview data only to construct a measure of social class.

10.

11.

12.


the fall semester of the eighth or ninth grade; at Wave 4, in the fall semester of the ninth or 10th grade.

SAMPLE

The sampling plan of the RYDS is designed to oversample youths at high risk for serious delinquency and drug use because the base rates for these behaviors are relatively low (Elliott et al., 1989; Wolfgang et al., 1987). To accomplish this goal while still being able to generalize the findings to a population of urban adolescents, the following strategy was used. First, the target population was limited to seventh- and eighth-grade stu- dents13 in the public schools of a city, Rochester, New York, which has a diverse population and a relatively high crime rate.14 Second, a stratified sample was selected from the target population so that (1) high-risk youths were overrepresented and (2) the findings could be weighted to represent the target population.

To accomplish the oversampling of high-risk youths, the sample was stratified on two dimensions. First, males were oversampled (75% versus 25%) because they are more likely than females to be chronic offenders and to engage in serious delinquency (Blumstein et al., 1986). Second, students from high-crime areas of the city were oversampled on the premise that crime rates and many delinquent opportunities are localized and that subjects living in areas with high crime rates are at greater risk for offending. To identify high-crime areas, we assigned each census tract in Rochester a resident arrest rate reflecting the proportion of the tract’s population arrested by the Rochester police in 1986.

A total of 4,013 students were enrolled in the seventh and eighth grades in spring 1988; of these, 3,372 (84%) were eligible for the sample.15 All eligible cases were assigned to their census tract of residence at the beginning of sample selection, To generate a final panel of 1,OOO students, we selected 1,334 on the basis of an estimated nonparticipation rate of approximately 25% (Elliott et al., 1983). The 1,334 cases were selected in the following way. First, students in the census tracts with the highest resident arrest rates, approximately the top one-third, were selected with cer- tainty. That is, all eligible students in these tracts were asked to

13. These ages were selected because they precede the major onset of official delinquency and drug use.

14. The city of Rochester had an index crime rate of 9,351 per 1OO.OOO inhabitants in 1986, considerably above the national rate (5,480). that of New York State (5,768). and even that of New York City (8,847) (Flanagan and Jamieson, 1988).

Students were considered ineligible if they moved out of the Rochester school district before the Wave 1 cases were fielded, if neither English nor Spanish was spoken in the home, if a sibling already was in the sample pool, or if they were older than the expected age for eighth-grade students under the Rochester schools’ admission policy.

15.

THORNBERRY ET AL.

participate in the study. Second, students in the remaining census tracts were selected at a rate proportionate to the tract's contribution to the overall resident arrest rate. Thus students from tracts with higher resident arrest rates are overrepresented in the sample because they are at greater risk for serious delinquency. Similarly, students in tracts with lower resident arrest rates are underrepresented. Once the number of students to be selected from a tract was determined, the student population in the tract was stratified by sex and grade, and students were selected from those strata at random. On the basis of these procedures, a final panel of 987 students and their families was selected for the study.16

Because the true probability of each adolescent being selected is known, the sample can be weighted to represent all seventh- and eighth-grade students in the Rochester public schools. The sample is weighted in the following analysis.

Current analysis is based on 841 adolescents who were interviewed at Waves 2 through 4; this number represents 85% of the base panel (N = 987). Subjects were lost primarily because of case attrition and seconda- rily because of missing data on the particular scales used here.17 Table 1 provides information on the demographic characteristics of the total panel of 987 and of these 841 students. Of the total panel, 74% are male and 26% female; 68% are African-American, 17% Hispanic, and 15% white; 75% were 13 or 14 years old at Wave 1; and many come from disadvan- taged homes, as evidenced by the high rates of welfare receipt (53%) and unemployment of the principal wage earner (34%). Comparison of the characteristics of respondents interviewed from Waves 2 through 4 with those of the total sample shows that attrition did not bias the sample (see Thornberry et al., 1993 for a more complete discussion of the sample and of case attrition). Throughout the remainder of this paper, we refer to Waves 2 through 4 as Times 1 through 3.

All face-to-face interviews were conducted by RYDS staff members. Adolescents generally were interviewed in private rooms provided by the

16. A comparison of the racelethnicity. grade, and gender characteristics of corn- pleted and noncompleted cases demonstrated that the final sample did not suffer from differential rates of refusal. Unfortunately, it was not possible to examine differences in other variables such as official delinquency, history of problem behavior, or family structure for those who participated and those who did not participate because we could not collect such information on the latter group. See Farnworth et al. (1990) for a complete description of the Rochester Youth Development Study sampling plan.

17. The overall retention rate is higher than 85%; 92% of the panel was interviewed at both Waves 3 and 4. The lower figures used in this analysis reflect the dual requirement that subjects be interviewed at all three waves and that they have complete data on the scales used in this analysis.


Table 1. Characteristics of the Total Sample and Subjects in Current Analysis (in percentages)

Total Subjects in Sample Current Analysis

Age at Wave 1 4 3 13.9 14.6

13 37.6 39.0 14 37.0 36.9

>14 11.5 9.5

Male 74.1 73.8 Female 25.9 26.2

African-American 68.1 69.6 Hispanic 17.1 16.1 White 14.8 14.3

Yes 53.0 51.6 No 47.0 48.4

Sex

Ethnicity

Welfare Receipt

Unemployment of Principal Wage Earner

Yes 33.8 32.7 No 66.2 67.3 N 987 841

schools; students not contacted at school were interviewed at home. Care- takers also were interviewed at home. Interviews with both adolescents and their caretakers were about an hour in length.

MEASUREMENT

The theoretical model contains four variables: Delinquent Peers, Peer Reactions, Delinquent Beliefs, and Delinquent Behavior. Each is measured with identical items at all three times.18

Delinquent Peers is measured by an eight-item scale. Respondents were asked how many of their friends had committed delinquent acts, ranging in seriousness from skipping classes to armed robbery, in the past six months.19 Response categories range from “most of them” (4) to

18. The text of the items and the means, standard deviations, and inter-item reliability coefficients for each scale are presented in Appendix 1.

19. To bound these time periods, the actual wording was “since the date of the last interview.” To help respondents focus on the appropriate time period, interviewers showed them a calendar, pointing out the date of their last interview and significant events (e.g., holidays or the beginning or end of the school year) that occurred since that time.


“none of them” (1). Cronbach’s alpha of inter-item reliability varies across the three times from .86 to .89.20

Peer Reactions measures the respondent’s perception of his or her friends’ reactions to the respondent’s delinquent behavior. Six items asked each respondent what his or her group of friends would say if they knew the respondent had committed delinquent behaviors ranging in seriousness from skipping classes without an excuse to armed robbery. Respondents’ perceptions of their friends’ reactions might be based on prior experience or on an inference from what they know about their friends. Response categories are “say it was okay” (3), “say nothing” (2), and “say it was wrong” (1). Cronbach’s alpha ranges from .80 to .83 across the three times.

An eight-item scale measures Delinquent Beliefs. Respondents were asked how wrong they think it is to commit the same types of delinquent behavior as contained in the other scales. Response categories range from “very wrong” (4) to “not wrong at all” (1). Coefficients of inter-item reliability range from .78 to .86 across the three times.

A total of 44 types of delinquent behavior and drug use are included in the self-report index. These items are derived from the National Youth Survey (Elliott et al., 1985a) as modified by the Denver Youth Survey (Huizinga et al., 1991). From the total set, 28 nonoverlapping iterns’zl ranging from running away from home to using a weapon to hurt sorne- one, are used to measure Delinquent Behavior. Drug use is not a part of this index, but selling drugs is included. All responses are screened to

.

20. Gottfredson and Hirschi (1990:156157) suggest that the observed correlation between peer delinquency and self-reported delinquency may exist because respondents’ reports of their friends’ delinquency is another measure of self-reported delinquency, since the two measures typically contain questions about the same behaviors. If their argument is correct, then factor analyzing the items used to create the indices should result in the identification of one factor with all or most of the items loading on that item. This is not the case for our data, however: as a result of a factor analysis using maximum-likelihood estimation procedures with oblique rotation, all the peer delinquency items loaded on one factor, and the self-reported delinquency items either loaded on a second factor or did not meet the criterion level (.a) on either factor. As further evidence that the two measures are not measuring the same construct, the correlation coefficients range from .50 to .59 over the three times. Although strong, these coefficients do not suggest that the two measures are identical. Similar findings have been reported by Agnew (1991a:56,1991b:144) and by Reed and Rose (1991:16). An alternative way to address Gottfredson and Hirschi’s argument is to use a measure reflecting peer misbehavior that does not contain items overlapping with those of the self-reported delinquency measure in the analysis. Results from such an analysis are reported below.

21. For example, items about shoplifting and theft of items of a certain value potentially can count the same event twice. In those instances, only one item is used to calculate the general delinquency score.


determine whether they fit the type of delinquency measured by the item and whether they are “actionable” offenses. The latter criterion is intended to screen out trivial offenses (e.g., siblings’ squabbles with one another in response to a question about serious assault), which law enforcement officials probably would ignore.** If the response meets these two criteria, the total frequency for each offense is counted to construct the summated delinquency index. Some subjects reported very high levels of delinquent activity, but for theoretical and practical purposes the difference between committing many acts of delinquency and committing an additional act is less important than the difference between initiating and adding to a fledgling career. Consequently we transform the delinquency index into its natural logarithm in the following analysis. Logging the delinquency variable minimizes differences at the high end of the scale in relation to differences at the low end.

In addition, because of their relationships to the theoretical variables, sex, race/ethnicity, age, and social class are included as control variables in this analysis. Sex is a dummy variable with females as the reference category. Race/ethnicity is measured with two dummy variables representing African-Americans and Hispanics; whites are the omitted category. Age is computed on the basis of information about the adolescent’s date of birth and the date of his or her first interview. l b o items from Wave 1 interviews with parents are used to construct a social class measure: welfare dependency and the primary wage earner’s employment status. The former asks whether the household received food stamps, Aid to Families with Dependent Children (AFDC), or other forms of financial aid from social services over the six months preceding the Wave 1 interview. The latter measures whether the primary wage earner was unemployed at the time of the first interview. If either question receives an affirmative answer, the household is classified as “lower social class.” Of the total of 841 households, slightly more than half (N = 458) are “lower social class” by this definition.

SPECIFICATION ISSUES

In an earlier section we presented the theoretical specification of the causal relationships among delinquent beliefs, peer associations, and delinquency at early adolescence. Four additional specification issues are discussed here.

22. To determine that the offenses reported are “actionable,” interviewers ask respondents to describe the most serious (or only) act committed in a category and to answer a number of follow-up questions about that act. Coders use this information to rate the act as actionable or not. The inter-rater reliability for the three times ranged from 90% to 95%. If the most serious delinquency described is not rated as delinquent, the item is coded 0.


First, at Time 1 we treat the variables as lagged endogenous variables and allow them t o correlate with each other. Causal relationships among them are not modeled.

Second, we predict one-wave stability effects for each variable, thus implying that the current level of each variable is expected to be produced, to some sizable extent, by prior levels of the same variable. Inclusion of stability effects in the model allows us to predict changes in Delinquent Behavior, Delinquent Peers, Peer Reactions, and Delinquent Beliefs from other lagged and contemporaneous variables. Furthermore, including both lagged and contemporaneous effects of predictor variables allows us to predict changes in endogenous variables from both level of predictor variables and changes in those variables.

Third, we include a contemporaneous causal loop including Delinquent Peers, Peer Reactions, and Delinquent Behavior at Xmes 2 and 3. Two of these variables-Delinquent Peers and Delinquent Behavior-are measured retrospectively and refer to the prior six months; the third variable-Peer Reactions-simply asks about perceptions of peer reactions with no explicit temporal referent. This procedure may produce a minor problem in the temporal order of the contemporaneous paths from Peer Reactions to Delinquent Behavior, We do not view this as a substantial problem, however, for two reasons. First, the respondent’s perception of peer reactions to delinquency is likely to be influenced heavily by recent past experiences with peer reactions to actual or potential behavior; therefore the question has an implicit prior referent. In addition, research on both self-reported delinquency (e.g., Menard and Elliott, 1990) and self- reported victimization ( e g , Garofalo and Hindelang, 1977; Woltman and Cadek, 1977) demonstrates a substantial recency effect in these types of measures. As a result, these measures probably contain a substantial degree of temporal overlap, which allows for the specification presented in Figure 1.23

Fourth, the model includes both contemporaneous and lagged effects. We predict contemporaneous effects to be substantially stronger than

~

23. To examine this issue further, we reestimated the model presented in Figure 1 when Peer Reactions was measured with data from Waves 1 through 3 instead of Waves 2 through 4. That change makes the contemporaneous paths from Peer Reactions to Delinquent Behavior unproblematic but creates a substantial problem in the temporal ordering of the contemporaneous paths from Delinquent Peers to Peer Reactions. In this model the coefficients from Delinquent Peers to Peer Reactions are reduced to insignificance but the coefficients from Peer Reactions to Delinquent Behavior remain substantially the same. These results further support the reasonableness of our assumption that a substantial temporal overlap exists between the measures of Peer Reactions and of Delinquent Behavior.


lagged effects because adolescents are far more likely to base their attitudes and behaviors on current influences than on past influences (also see Agnew, 1991b:131). Also, because the effects of these variables are expected to diminish over time, we include lagged effects from only the immediately prior time.

ESTIMATION

We estimate equations for this analysis using LISREL 7, a program for full information maximum likelihood (FIML) covariance structure analysis (Joreskog and Sorbom, 1989). A matrix of variances-covariances is analyzed. Inferences about statistical significance are based on unstandardized coefficients; for the sake of comparison, standardized structural coefficients are presented here. Because we make directional h ypotheses-all effects are expected to be positive-we employ one-tailed tests.

It is often difficult to estimate models as complex as the one presented in Figure 1 because one must balance the sometimes competing demands of retaining fidelity to the theoretical model and gaining sufficient degrees of freedom to estimate parameters such as correlations among error terms. As a result, the analysis proceeds incrementally. We begin by estimating the model presented in Figure 1, which is theoretically closest to the model originally specified in interactional theory (Thornberry, 1987). This model does not estimate correlated error terms, and it assumes that the equations for Delinquent Beliefs are block-recursive with respect to the other variables. Then we estimate a model in which the block-recursive assumption is partially relaxed, and compare its results with those of the first model. Finally, we estimate a model in which the error terms for all the variables included in the causal loop are allowed to correlate. To estimate the latter two models, we must impose additional constraints on the model; these are described below.

Before we estimated the models, we verified that all equations were identified, using the algorithm presented by Maddala (1988:301-3O4). First, in the case of the model in Figure 1, we assumed that the equations for Delinquent Beliefs were block-recursive with respect to the other variables and, by definition, were identified (Berry, 1984:84-86). Second, all other equations in the model met the rank condition for identification; this condition is both necessary and sufficient to prove identification. The rank condition also was met for Models 2 and 3, which we present below.


RESULTS

MODEL 1

Structural coefficients and goodness-of-fit statistics for the model in Fig- ure 1, referred to as Model 1, are presented in Figure 2 and Table 2. The traditional chi-square goodness-of-fit test for the model is significant (chi- square = 275.12, d.f. = 30, p < .001), an indication that the model-based and the observed covariance matrices are different. This outcome is not surprising, however, in view of the sensitivity of chi-square to large sample sizes such as the one used here (N = 841). LISREL’s goodness-of-fit sta- tistic indicates a much better fit between the model and the data; this sta- tistic is .962, close to the upper limit of 1.00 and well within the acceptable range.

Table 2. Causal Relationships among Delinquent Peers, Peer Reactions, Delinquent Beliefs, and Delinquent Be haviora

Stability Effects Peersl -> Peers2 Peer Reactions1 -> Peer Reactions2 Behaviorl -> Behavior2 Beliefsl -> Beliefs2 Peers2 --> Peers3 Peer Reactions2 -> Peer

Behavior2 --> Behavior3 Beliefs2 -> Beliefs3

Cross-Lagged Effects Peersl --> Peer Reactions2 Peersl --> Beliefs2 Behaviorl -> Peers2 Behaviorl -> Beliefs2 Beliefsl -> Peers2 Beliefsl -> Behavior2 Peers2 --> Peer Reactions3 Peers2 --> Beliefs3 Behavior2 -> Peers3 Behavior2 -> Beliefs3 Beliefs2 -> Peers3 Beliefs2 -> Behavior3

Reactions3

Model 1

.446*

.432*

.548*

.432*

.382*

.400*

.408*

.538*

-.069 -.012 -.024

.067*

.074*

.116* -.078 -.011

.134*

.007

.097*

.055*

Model 2

.446*

.427*

.548*

.436*

.382*

.402*

.408*

.541*

-.070 -.007 -.024

.062

.074*

.116* -.080 -.008

.134*

.001

.097*

.055*

Model 3

.437*

.413*

.498*

.438*

.302*

.305*

.432*

.540*

-.001

.074*

.061*

.084*

-.002

.002

.047

.072*

PEERS, BELIEFS, AND DELINQUENCY

Contemporaneous Effects Peers2 -> Peer Reactions2 Peers2 -> Beliefs2 Peer Reactions2 ->

Behavior2 -> Peers2 Behavior2 -> Beliefs2 Peers3 -> Peer Reactions3 Peers3 -> Beliefs3 Peer Reactions3 ->

Behavior3 -> Peers3 Behavior3 -> Beliefs3

Peers2 <-> Peer Reactions2 Peers2 <-> Behavior2 Peer Reactions2 <->

Peer Reactions2 <--> Beliefs2 Peers3 <-> Peer Reactions3 Peers3 <-> Behavior3 Peer Reactions3 <-->

Peer Reactions3 <-> Beliefs3

Chi-square d.f. P Goodness-of-Fi t N = 841

Behavior2

Behavior3

Correlated Errors

Behavior2

Behavior3

Goodness-of-Fit Statistics

.445*

.215*

.179*

.150*

.113*

.526*

.269*

.311*

. O N *

.056*

275.12 30 .001 .%2

.443*

.217*

.179*

.150*

.loo*

.525*

.274*

.312*

.081*

.043

.050*

.027

266.37 28 .001 .964

67

.363*

.218*

.357*

.159*

.077*

.661*

.266*

.222*

.492*

.05 1

.048 -.072 -.111*

.053* -.186* -.218*

.038

.022

255.86 26 .001 .966

*p <.05 a Significance tests are one-tailed for structural coefficients and two-tailed for correlated errors.

Throughout this analysis, sex, social class, and race/ethnicity (measured by two dummy variables) are held constant. These effects are not presented in Figure 2 or Table 2 because they add considerable visual clut- ter. Each model has a total of 32 coefficients. Few are statistically significant; for Model 1.27 of the 32 effects are statistically insignificant. Effects which are significant in at least one of the models are presented in Appen- dix 2. At Time 2, African-Americans report significantly higher levels of Peer Delinquency (.08) than do whites, and respondents of higher social class report lower levels of peer delinquency (-.05). At Time 3, males report significantly higher levels of Peer Delinquency than do females (.13), African-Americans report less Delinquent Behavior (-.07) than do whites, and those of higher social class report higher levels of Peer Reac- tions (.05).*4

24. Even fewer significant effects appear in Models 2 and 3, as shown in Appendix

Figu

re 2

. Em

piric

al M

odel

of C

ausa

l Rel

atio

nshi

ps a

mon

g D

elin

quen

t Pee

rs,

Peer

Rea

ctio

ns, D

elin

quen

t Bel

iefs

, and

Del

inqu

ent B

ehav

ior

P r

*p<.

os

e e


As expected, all the stability effects are positive and significant, an indication that the prior level of each of these variables exerts a causal impact on the current levels of the same variable. The theoretically most central hypotheses are tested by examining the effect of variables on each other. We begin discussion by examining the peer association-delinquent behavior nexus, and then proceed to the role of delinquent beliefs in the model.

PEERS AND BEHAVIOR

Interactional theory hypothesizes that associating with delinquent peers and engaging in delinquency are related reciprocally. The results presented in Figure 2 offer strong support for this assertion. These results also suggest that, as hypothesized, the effects are primarily contemporaneous; only one of the lagged effects is significant.

The first part of the reciprocal hypothesis posits that associating with delinquent peers has an indirect effect on delinquent behavior via the variable of peer reactions. At Time 2, changes in Delinquent Peers are related positively to changes in Peer Reactions (.445); in turn, Peer Reactions positively affects changes in Delinquent Behavior (.179). At Time 2, the indirect effect of Delinquent Peers on Delinquent Behavior, via Peer Reactions, is .079. The reciprocal hypothesis posits a direct effect from changes in Delinquent Behavior to changes in Delinquent Peers; this is also seen at Time 2 (.150). Thus the hypothesized causal loop is consistent with these data?

From Time 2 to Time 3 we find a lagged effect of Delinquent Behavior on Delinquent Peers (.134); then the causal loop relating these variables begins again. At Time 3, changes in Delinquent Peers lead to changes in Peer Reactions (S26); in turn, Peer Reactions has a positive effect on changes in Delinquent Behavior (.311). The latter effect is statistically larger than its Time 2 counterpart. Here the indirect effect from Delin- quent Peers to Delinquent Behavior is .163, twice as large as the indirect effect at Time 2. The reciprocal hypothesis, that engaging in delinquency increases the chances of associating with delinquent peers, is also sup- ported at Time 3. Changes in Delinquent Behavior have a positive effect on changes in Delinquent Peers (.081).

2. Moreover, all of the effects that are nonsignificant in Model 1 remain nonsignificant in Models 2 and 3.

In a recursive model, the difference between 1 and the standardized residual variance in an equation is the squared multiple correlation coefficient (i.e.. R2) associated with the equation. In a nonrecursive system, however, R2 values for the equations cannot be calculated (Bentler, 1989). In the present model, delinquent peers, peer reactions, and delinquent behavior form a causal loop; therefore the model must be considered nonrecursive. As a result, R2 values cannot be interpreted meaningfully and therefore are not discussed in the text.

25.


In general, these results are quite consistent with a reciprocal specification of the relationship between peers and behavior. Associating with delinquent peers tends to increase delinquency, and at least part of this effect is mediated through perceptions of positive reinforcements for delinquency. In turn, engaging in delinquency exerts a positive effect on associating with delinquent peers. Almost all of these effects are contemporaneous rather than lagged; this finding suggests that recent experiences are more central than distant experiences in explaining both peer affiliations and behavior. Therefore these results indicate that both socialization and selection processes are at play in accounting for the development of delinquency over time.

To this point, the results for the relationship between associating with delinquent peers and engaging in delinquency are consistent with an interactional perspective. Gottfredson and Hirschi (1990), however, suggest that this relationship is not causal but a method effect created by the two measures including items about the same behaviors. To examine this contention further, we reestimate our model, substituting measures of peer drug use and peer reactions to drug use for peer delinquency and peer reactions to delinquency; the other variables remain the same.26 Thus the peer and the behavior variables now refer to different forms of deviant behavior. The results for the “drug model” are virtually identical to those shown in Figure 2. The only substantively meaningful change is that the cross-lagged effect from Time 1 Delinquent Behavior to T m e 2 Drug Using Peers is significant, whereas the contemporaneous effect at Time 2 between these two constructs is not. The significant cross-lagged effect from Delinquent Behavior to Drug Using Peers is consistent with previous research (Elliott et al., 1989), which found that delinquent behavior generally occurs before the initiation of drug use. Hence we would expect delinquent behavior to occur before the association with peers who use drugs. The central point, however, is that this analysis does not support Gottfred- son and Hirschi’s contention that the relationship between delinquent peers and self-reported delinquency is simply a methodological artifact. When the behavioral overlap is eliminated, the hypothesized structural effects are still observed.

DELINQUENT BELIEFS

At middle adolescence, interactional theory hypothesizes that Delin- quent Beliefs has a reciprocal relationship with both Delinquent Peers and Delinquent Behavior. The results presented in Figure 2 generally support this hypothesis.

From Time 1 to Time 2, Delinquent Behavior has a significant lagged

26. Results are available on request.


impact on Delinquent Beliefs (.067), and Delinquent Beliefs has lagged effects on both Delinquent Peers (.074) and Delinquent Behavior (.116). At Time 2, these variables then exert a contemporaneous effect on Delin- quent Beliefs; the effect is .215 from Delinquent Peers and .113 from Delinquent Behavior. This process then repeats itself from Time 2 to Time 3. We find lagged effects from Delinquent Beliefs to Delinquent Peers (.097) and Delinquent Behavior (.055), and then contemporaneous effects from Delinquent Peers (.269) and Delinquent Behavior (.056) to Delinquent Beliefs. Overall it appears that Delinquent Beliefs increases the likelihood of associating with delinquent peers and engaging in delinquency. In turn, these associations and behaviors lead to the hardening of a prodelinquent belief structure.

MODEL 2

The model estimated above assumes that the Delinquent Beliefs equation is block-recursive with respect to the other three equations at each time period. This specification implies that errors in predicting Delin- quent Beliefs are not correlated with errors in predicting the other three equations. This is a fundamental assumption in path-analytic models. Of course, the variables are measured in the same interview for each subject; as a result, the errors in Delinquent Beliefs could be correlated with the other three variables. If errors in Delinquent Beliefs are correlated with errors in other equations, the structural parameters reported could be incorrect. To test whether this is so, Model 2 somewhat relaxes the block- recursive assumption.

Without changing the theoretical specification, we can estimate the correlation between the error terms for Delinquent Beliefs and for one of the three loop variables at each time period. Delinquent Peers and Delin- quent Behavior are behavioral measures, whereas Peer Reactions and Delinquent Beliefs are cognitive or perceptual variables. As a result, we are most skeptical about the block-recursive assumption that errors in predicting Delinquent Beliefs are not correlated with errors in Peer Reactions because they both tap the subjects’ cognition and therefore could be sys- tematically and simultaneously over- or under-reported. Therefore Model 2 estimates the Delinquent Beliefs-Peer Reactions error correlation. Results are presented in Table 2.

The chi-square for Model 2 shows a statistically significant improvement over the chi-square for Model 1. At l3me 2 the error correlation between Delinquent Beliefs-Peer Reactions is positive and statistically significant, but very small (.05). It is not statistically significant at Time 3. In light of this finding, it is not surprising that the structural parameters in the two models are nearly identical, as is evident in a comparison of the first two


columns in Table 2. The only coefficient for which perhaps there is a substantive change is for the contemporaneous effect of Delinquent Behavior on Delinquent Beliefs at Time 3. In Model 1 it is .056 and significant (t = 1.84), but in Model 2 it is .043 and fails to attain statistical significance (t = 1.36). Aside from this modest change, the other parameter estimates are quite similar. The substantive results for the causal loop remain unchanged. At both time periods, Delinquent Peers affect Peer Reac- tions, which affect Delinquent Behavior in turn. Delinquent Behavior then has a reciprocal effect on Delinquent Peers.

MODEL 3

Neither of the models reported thus far has estimated correlations among the errors in the three equations in the causal loop. By the very nature of nonrecursive systems, however, one would expect the errors across equations to be correlated. In our model, these errors cannot be estimated without acquiring additional degrees of freedom in the system.

Model 3 estimates a model allowing for correlated errors among all variables in the causal loops while also including the error correlation between Delinquent Beliefs and Peer Reactions. Results for this model are presented in Table 2. To estimate this model, we must omit two cross- lagged effects at each time period: the effects of Delinquent Peers at one time on Peer Reactions at the next time, and the effects of Delinquent Behavior at one time on Delinquent Peers at the next time. Three of the four omitted effects were not statistically significant in Models 1 and 2; only the effect of Delinquent Behavior at Time 2 on Delinquent Peers at Time 3 was significant. The loss of this parameter, however, is a cost of estimating the correlations of errors across equations.

Of the eight error correlations estimated in this model, four are statistically significant but all are small. As in the previous model, the error correlation between Delinquent Beliefs and Peer Reactions is statistically significant and small at l i m e 2 but insignificant at Time 3. At l ime 2 we find a significant correlation between Peer Reactions and Delinquent Behavior (-.lll), and at Time 3 between Peer Reactions and Delinquent Peers (-.186). Recall that in order to estimate this model we eliminated the cross-lagged effect of Delinquent Peers at one time on Peer Reactions at the next time. Models 1 and 2 in Table 2 show that the omitted effects are negative. In addition, Delinquent Peers has lagged positive indirect effects on Delinquent Behavior at Time 2 and at Time 3. This is precisely the state of affairs that would exist in order to create significant and negatively correlated errors between two equations: an omitted negative effect of a variable on one equation and a positive effect of that variable on the other equation. Consequently, if these effects could have been included, these error correlations might not have been different from 0. At Time 3,


the correlation between Delinquent Peers and Delinquent Behavior (-.218) is also statistically significant and negative.

Although half of the error correlations are significant, and some are negative, they are all small. The largest coefficient is -.218, an indication that the error terms have no more than 4.7% of the variance in common. In view of this finding, it is not surprising that the structural coefficients do not change greatly from Model 2 to Model 3 and that only three sizable changes seem to have taken place. The lagged effect from Delinquent Beliefs at Time 2 to Delinquent Peers at Time 3 drops from .097 (p < .05) to .047 (ns). On the other hand, two significant effects increase in magni- tude when correlated errors are estimated. The contemporaneous effect from Peer Reactions to Delinquent Behavior at Time 2 increases from .179 to .357, and the effect of Delinquent Behavior on Delinquent Peers at Time 3 increases from .081 to .492. The second change might have been caused by the deletion of the cross-lagged effect from Delinquent Behav- ior at Time 2 to Delinquent Peers at Time 3. Because that effect was significant in the previous models, Model 3 essentially forces the contemporaneous effect of Delinquent Behavior at Time 3 to account as well for the lagged effect of Delinquent Behavior on Delinquent Peers. As a result, perhaps it is reasonable to interpret the coefficients reported in Model 2 and Model 3 as representing the lower and upper bounds for this coefficient.

Although substantial changes were made in the specification of Model 3 as compared with Model 1-four cross-lagged effects were deleted and eight error correlations were added-the overall pattern of results remains quite stable. Evidence for the reciprocal relationship between peer associations and delinquency remains. Although the magnitudes of some effects change, peer associations continue to affect delinquent behavior via the reactions of peers; in turn, behavior continues to affect peer affiliations. Also, the delinquent beliefs variable plays much the same role in Model 1 as in Model 3.

DISCUSSION

Three general perspectives-the socialization, the selection, and the interactional perspectives-have been employed by social scientists to explain the observed consistency between a person’s behavior and his or her social environment. In this article we have examined empirical support for these divergent perspectives in the development of delinquency. Specifically, we examined relationships among associating with delinquent peers, adopting delinquent beliefs, and engaging in delinquency. For these variables, the socialization perspective, which derives from differential association and social learning theory, grants causal priority to peer


associations. The selection perspective, which derives from social control theory, grants causal priority to delinquent behavior. The interactional perspective, which cortibines aspects of the other two perspectives, argues that social process variables such as these are related reciprocally over the life course.

Results from the panel model estimated here clearly support the underlying premise of interactional theory. As hypothesized by that model, associating with delinquent peers leads to increases in delinquency via the reinforcing environment provided by the peer network. In turn, engaging in delinquency leads to increases in associations with delinquent peers. As this process unfolds over the life course, delinquent youths are apt to associate with peers who also are delinquent, and those peers increase the likelihood of further delinquent behavior. On the other hand, conforming youths are apt to associate with peers who also are conforming; those peers increase the likelihood of further conforming behavior.

The developmental perspective of interactional theory hypothesizes that the role of delinquent beliefs varies developmentally. At early adolescence, these beliefs are thought to be primarily effects of peers and behavior, whereas at middle adolescence, these variables are thought to be related reciprocally. The results presented here, which cover the ages associated with the transition from early to middle adolescence, generally support only the latter specification. Delinquent beliefs tend to increase association with delinquent peers and involvement in delinquency; in turn these variables tend to consolidate further a delinquent belief structure. Delinquent Peers has a substantially larger impact on the formation of Delinquent Beliefs than does Delinquent Behavior. This finding suggests that the delinquent peer network, with its normative support for delinquency, may be particularly important in accounting for both the attitudi- nal and the behavioral patterns of the individual.

The results of this analysis are inconsistent with both the socialization and the selection perspectives. The unidirectional hypotheses of these perspectives are not compatible with the consistently observed causal loops observed for the peer-behavior relationship. To examine this issue further, we estimated two additional models-one for a socialization model, in which the paths from delinquency to delinquent peers were deleted, and the other for a selection model, in which the two-step path from delinquent peers to delinquency was deleted. Neither model con- verged to a solution; this outcome suggests that bidirectional relationships are needed.27

In sum, the empirical results presented here support the central contention of interactional theory: delinquent behavior is part and parcel of a

27. Results are available on request.


dynamic social process rather than simply an outcome of that process, as it is often portrayed in criminological theories such as recent integrated models (e.g., Elliott et al., 1985a; Johnson, 1979). Although delinquency is influenced by peer associations and delinquent beliefs, it also influences those associations and beliefs to create behavioral trajectories toward increasing delinquency for some youths and toward increasing conformity for others.

These findings have implications for both criminological theory and research. Theoretically, the long-standing debate between differential association or learning theory on the one hand and social control theory on the other may have unnecessarily occupied the time of theoretical criminology. The issue should not be posed as a question of whether associations with delinquent peers cause delinquency, or whether adolescents, once having committed delinquent behavior, seek out and associate with others who engage in similar behaviors. Choosing either of these models leads to a perspective that is only half right. The present results suggest the need to specify how the reciprocal relationship between these variables develops as adolescents proceed from the initiation of delinquency to its maintenance and eventual cessation.

Methodologically, this study supports the contention of researchers who have emphasized the need for longitudinal studies to understand more clearly the development of delinquency (e.g., Farrington et al., 1986). It is evident that the type of relationships specified in the current study could not have been generated from cross-sectional research. Indeed, these results lead us to agree with Matsueda’s (1989:44345) rejoinder to Hir- schi and Gottfredson’s (1983573) assertion that longitudinal research designs are “unjustified and potentially misleading.” As Matsueda suggests, what may be unjustified and potentially misleading are cross-sectional designs and the inferences based on those designs.

Finally, recent studies, including the current one, suggest that the causal processes among variables central to traditional theories of delinquency are much more complex than those theories have depicted them to be. Although models incorporating reciprocal relationships require data that are more difficult and costly to acquire and more difficult to analyze, they also make more intuitive sense. It is time for criminological theories to reflect the complexity of interrelationships observed in the everyday world.

These findings also have more general implications for the understanding of human behavior. If these results are replicated in other behavioral areas, such replication would suggest that a more dynamic perspective is needed to model and understand human behavior because these findings


suggest that the “socialization explanations, reflexively tendered by soci- ologists to explain similarities between man and his environment” (Liska, 1973:24), may be inadequate and misleading.

A central strain of thought in the social sciences holds that human behavior is characterized by interactions between individuals and their environment; it is not simply the case that the social environment acts on the individual to create behavior patterns (e.g., Homans, 1961; Magnus- son, 1988). The results of our investigation are certainly consistent with this view. They suggest that peers and focal subjects interact with each other over the life course, that selection of peers influences beliefs and behavior, and that the individual’s beliefs and behavior have consequences, one of which is to influence the selection of peers. To look solely at either one of these processes as if it offered a complete explanation for human behavior is incomplete and misleading. That certainly appears to be the case for delinquent behavior, and it probably holds in other behavioral realms as well. If this is so, these findings imply that inquiry into the etiology of human behavior would be well advised to adopt more dynamic theoretical models when they attempt to explain the consistency between individuals and their social environment.

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Terence P. Thornberry is Professor and former Dean in the School of Criminal Jus- tice at the University at Albany. His major research interests are in developing and testing theories of delinquency and drug use, especially from a longitudinal perspective.

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Marvin D. Krohn is Professor and Department Chair in the Department of Sociology at the University at Albany. His research interests include the investigation of social psychological theories of adolescent substance abuse and delinquent behavior. He is cl;rrently involved in a panel study of inner-city youth designed to examine hypotheses derived from those perspectives.

Margaret Farnworth is Associate Professor and Associate Dean in the College of Criminal Justice at Sam Houston State University. Her major research interests are social stratification and crime and criminal court processing.

Sung Joon Jang is Assistant Professor in the Department of Sociology at The Ohio State University. His major research interests are in developing and testing theories on social deviance, especially crime and delinquency, based on quantitative research methods. He is currently exploring a developmental approach to the etiology of delinquency.

App

endi

x 1.

M

eans

, Sta

ndar

d D

evia

tions

, and

Rel

iabi

lity

Coe

ffic

ient

s of

Sca

les

for

Tim

es 1

, 2,

and

3

Tim

e 1

Tim

e 2

Tim

e 3

-

---

Item

s

Del

inqu

ent

Peer

s Si

nce

we i

nter

view

ed y

ou th

e la

st tim

e, h

ow m

any

of t

hese

frie

nds.

. .

1.

Use

d a

wea

pon

or f

orce

to

get

mon

ey o

r th

ings

fro

m p

eopl

e?

X

= 11

.15

11.2

8 11

.22

2.

Atta

cked

som

eone

with

a w

eapo

n or

with

the

idea

of

serio

usly

s.

d.

=

3.90

A.

27

4.16

hu

rting

the

m?

Alp

ha =

.86

.89

.87

3.

Hit

som

eone

with

the

ide

a of

hur

ting

them

? 4.

St

ole

som

ethi

ng w

orth

mor

e th

an $

loo?

5.

St

ole

som

ethi

ng w

orth

mor

e th

an $

5 bu

t le

ss th

an $

so?

6.

Dam

aged

or

dest

roye

d so

meo

ne e

lse’

s pr

oper

ty o

n pu

rpos

e?

7.

Took

a c

ar o

r mot

orcy

cle

for

a rid

e or

driv

e w

ithou

t th

e ow

ner’

s pe

rmis

sion

? 8.

Sk

ippe

d cl

asse

s w

ithou

t an

exc

use?

Peer

Rea

ctio

ns

Wha

t wou

ld y

our

frie

nds

say

if yo

u. .

. 1.

U

sed

a w

eapo

n or

for

ce t

o ge

t m

oney

or

thin

gs f

rom

peo

ple?

8

= 7.7

8 8.

00

7.95

2.

H

it so

meo

ne w

ith t

he id

ea o

f hu

rting

the

m?

s.d.

= 2.

08

2.38

2.

28

3.

Stol

e so

met

hing

wor

th $

SO?

Alp

ha =

.8

0 .8

2 .8

3 4.

D

amag

ed o

r de

stro

yed

som

eone

els

e’s

prop

erty

on

purp

ose?

5.

To

ok a

car

or

mot

orcy

cle

for

a rid

e or

driv

e w

ithou

t th

e ow

ner’

s pe

rmis

sion

? 6.

Sk

ippe

d cl

asse

s w

ithou

t an

exc

use?

% m

U

m c z I

0 c m

Z c)

App

endi

x 1.

M

eans

, Sta

ndar

d D

evia

tions

, and

Rel

iabi

lity

Coe

ffic

ient

s of

Sca

les

for

Tim

es 1

, 2,

and

3 (c

ont.)

Ti

me

1 Ti

me

2 Ti

me

3 ---

Item

s D

elin

quen

t B

elie

fs

How

wro

ng d

o yo

u th

ink

it is

to. .

.

-

1.

Stea

l som

ethi

ng w

orth

$lo

o?

X =

10

.21

10.4

7 10

.48

2.

Use

a w

eapo

n or

for

ce t

o ge

t m

oney

or

thin

gs f

rom

peo

ple?

s.

d.

=

2.97

3.

16

2.91

4.

Hit

som

eone

with

the

idea

of

hurt

ing

them

? 5.

Ta

ke a

car

or m

otor

cycl

e fo

r a

ride

with

out

the

owne

r's p

erm

issi

on?

6.

Stea

l som

ethi

ng w

orth

$SO?

7.

Purp

osel

y da

mag

e or

des

troy

pro

pert

y th

at d

oes

not

belo

ng t

o yo

u?

8.

Skip

scho

ol w

ithou

t an

exc

use?

3.

Atta

ck s

omeo

ne w

ith a

wea

pon

or w

ith t

he id

ea o

f se

rious

ly h

urtin

g th

em?

Alp

ha =

.I

8 .8

6 .8

5

00

h, 2 0

;rr

Z


Appendix 2. Effects of Background Variables on Endogenous Variables for Significant Relationships in Any of the Three Models

Model 1 Model 2 Model 3 - - Time 2

African-American -> Peers2 .08* .08 .07 Social Class -> Peers2 -.05* -.05 -.05

Time 3 African-American -> Delinquency3 -.07* -.07 -.06 Social Class -> Peer Reactions3 .05* .05* .05 * Male -> Peers3 .13* .13* .11*

*p < .05

Documents

DELINQUENT PEERS, BELIEFS, AND DELINQUENT BEHAVIOR: A LONGITUDINAL TEST OF INTERACTIONAL THEORY