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Reconsidering Peer Influences on Delinquency: Do Less Proximate Contacts Matter?

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Abstract

Much research on adolescent delinquency pivots on the notion of peer influence. The peer effect that is typically employed emphasizes the transmission of behaviors and attitudes between adolescents who are directly linked. In this paper, we argue that to rely solely on those direct social ties to capture peer influence oversimplifies the realities of adolescent society. We use data from the National Longitudinal Study of Adolescent Health to show that indirect peer relations can exercise independent influences on adolescent delinquency. Adolescents actively draw on the examples of friends of friends, and even more distal peers, as they develop their repertoires of action and identity. We argue, however, that this behavior actually reflects adolescents’ ongoing struggle to impress their closest friends and to preserve their social circle. Indeed, the extent to which adolescents are willing to model the behavior of indirect contacts seems to decline as that behavior becomes more dissimilar from that of their close friends. Our findings dovetail with an account of the adolescent as a rational actor who struggles for social acceptance in a complex peer environment which offers conflicting behavioral models.

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Notes

  1. For a more elaborate discussion of differential association theory, refer to Akers (1998) and Warr (2001), and refer to Akers (1998) for a more detailed discussion of social learning theory.

  2. Studies tend to: (1) employ a measure of differential association that captures adolescents’ perceptions of their friends’ delinquency rather than friends’ actual delinquency; (2) examine the effect of peer influence on substance use or cigarette smoking rather than more serious forms of delinquency; and (3) rely on samples for select places rather than ones that are nationally representative.

  3. Urberg’s (1992) examination of adolescent substance use provides the only analysis of this sort to date. She finds that an adolescent’s best friend, rather than the larger social crowd, has the main influence on cigarette smoking. However, her study is based on data for a sample of adolescents from a single school system, and thus, may not be generalizable.

  4. Because the emphasis here is on peer pressure, which is closely aligned with potential rewards and sanctions from friends, the conceptualization of social influence is closely related to Akers’ concepts of imitation and reinforcement. Adolescents are engaging in behavior resembling that of more distant peers because they are considering the consequence of that behavior—in this case, the consequence is approval or disapproval of close friends.

  5. Initially, 160 schools were selected for the study, though some elected not to participate. Of the 132 schools comprising the main sample, two do not have sample weights, resulting in a usable sample of 130 schools. Among these, 112 had response rates sufficient for global network data. According to Moody (2001), this restricted sample is generally representative of all schools selected, and sample selection effects appear to be negligible.

  6. The 10-nomination limit incorporates most friendships, as the average number of nominations for this sample was only 4.15 (Haynie 2001; Moody 2001), paralleling previous findings that close friendship groups typically include five or six people (see Cotterell 1996).

  7. The original responses are ordinal in nature, but to facilitate comparability with recent research, we use Haynie’s (2001) dichotomized version.

  8. The wording of the questions about delinquency involvement, combined with the schedule of interviews for the Add Health study, means that T2 delinquency may have taken place prior to some of the predictors in the analysis. However, research on survey methodology (Blair and Burton, 1987; Burton and Blair 1991) suggests that respondents usually reference the more recent side of an expansive time frame when answering questions about the frequency of their own behavior. Thus, it is likely that the adolescents extrapolate from more recent behavior patterns to provide a picture of their behavior during the past year. In addition, there is a considerable period during which there is no overlap between the surveys. Nonetheless, future research should carefully consider the assumptions that go into using this measure.

  9. While the correlation between risk-taking behaviors and more serious delinquency is far from perfect (r =0.47 in the saturation sample, which is a subset of schools wherein adolescents’ friends do answer questions about more serious delinquency), it is nonetheless statistically significant, which provides some support for using this measure with a larger, more representative sample. The saturation sample is limited to specific schools that were selected for their size (two very large schools and ten small schools) and comparisons between it and the core sample reveal that the saturation sample may under-represent adolescents from large schools (Haynie 2002). Moreover, as Haynie (2001) notes, using the risk-taking index may actually provide a more conservative estimate of peer effects on adolescent delinquency. Further, Osgood et al. (1988) offer evidence for the generality of deviance, which provides some justification for the use of this measure.

  10. Instead of using merely the mean of friends’ behaviors, a diffusion approach might choose to model a decaying social distance function (where the effect of j’s behaviors on i’s delinquency is stronger when j is more proximate to i in the network). Myers (1997) models a similar decaying “contagion” effect of race riots in one U.S. city on the likelihood of race riots in another, measured as a function of increasing geographic distance between them. This is an amalgamated measure in that the effects of all the peers in one’s network, no matter how proximate, are considered together in one variable. This measure is attractive as an extension of the traditional peer effects variable, but it does not allow one to examine how the relationship between proximity and influence changes as proximity increases.

  11. We consider these steps for two reasons: (1) some schools had few students, so the number of steps separating adolescents decreased as a function of school size (considering those with more distant ties would reduce the sample size and increase sample selection); and (2) it became clear early on that considering steps beyond three steps or more offered little additional information. More importantly, as a reviewer thoughtfully pointed out, adolescents probably do not define their peer relations in such detail as to justify numerous geodesic steps—four steps, five steps–away from them. We do want to highlight, however, that the number of direct ties in adolescents’ networks is very small compared to the number of indirect ties.

  12. These values are calculated on the sent/received network using various SAS programming modules, available in SPAN (Moody 1999).

  13. Bivariate correlations are available from the authors upon request. Examination of these correlations, as well as Variance Inflation Factor scores, revealed that the only high correlation in the model is between the interaction terms and their respective components.

  14. The quadratic age term allows us to test for a non-linear effect of age on delinquency, an effect found in prior research that examines the age-crime curve at the individual and aggregate levels (Farrington 1986; Jang 1999; Warr 1993).

  15. Negative binomial regression expresses effects in terms of event rates, such that a one-unit increase in the independent variable leads to a percentage change in the rate of the dependent variable: =100[exp(β)−1] (Long 1997).

  16. Despite such a small percentage of direct friendship ties, it is true that adolescents exert the most time and effort maintaining these close friendships; as Table 4 illustrates, the relatively small percentage of close versus distant ties does not belie their significance.

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Acknowledgments

We thankfully acknowledge the technical and editorial assistance of Ruth Peterson, Dana Haynie, and James Moody, as well as invaluable editorial suggestions for early drafts from Lauren Krivo, Lisa Keister, and Donna Bobbit-Zeher. We are also grateful to the editor and anonymous reviewers for comments on earlier drafts of the manuscript. This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronal R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (http://www.cps.unc.edu/addhealth/contract.html).

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  1. Department of Sociology, Ohio State University, 300 Bricker Hall, Columbus, OH, 43210, USA

    Danielle C. Payne

  2. Department of Sociology, University of Chicago, Chicago, IL, USA

    Benjamin Cornwell

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Correspondence to Danielle C. Payne.

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Payne, D.C., Cornwell, B. Reconsidering Peer Influences on Delinquency: Do Less Proximate Contacts Matter?. J Quant Criminol 23, 127–149 (2007). https://doi.org/10.1007/s10940-006-9022-y

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