Hanging Out with the Wrong Crowd? The Role of Unstructured Socializing in Adolescents’ Specialization in Delinquency and Substance Use

Abstract

Objectives

Despite abundant attention to offending specialization in criminology, scholars have only recently started to explore opportunity-driven explanations for within-individual patterns of specialization. The current study examines whether unstructured socializing with specific friends can explain within-individual changes in adolescents’ degree of specialization in delinquency and substance use.

Methods

Data were derived from the PROSPER Peers Project, a longitudinal study consisting of five waves of data on 11,183 adolescents (aged 10 to 17). The data include self-reports about engagement in delinquency and substance use, sociometric information, and information on the time respondents reported spending in unstructured socializing with their nominated friends. Hypotheses were tested with negative binomial and binomial logit multilevel models.

Results

The findings indicate that involvement in unstructured socializing with friends who steal, vandalize, commit violence, use alcohol, use cigarettes, or use drugs enhances adolescents’ risks for engagement in those respective behaviors. Such activity affects adolescents’ quantitative engagement as well as their level of specialization in these behaviors.

Conclusions

The study indicates that routine activity—in particular involvement in unstructured socializing—explains within-individual changes in deviance specialization among adolescents. Thus, exposure to opportunities can explain why adolescents specialize in certain types of delinquency and substance use in one time-period, and in other types of behavior in other time-periods. This adds a proximate explanation for this phenomenon to other explanations that focus on local life circumstances and peer group affiliation.

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Notes

  1. 1.

    We follow the approach of McGloin et al. (2007) and Sullivan et al. (2006) by studying whether deviant acts committed by one individual in the same time window are of the same type. This interpretation of specialization differs from the one used in older work, which focused on whether chronologically ordered deviant acts would be of the same type (Farrington, 1986, Paternoster et al. 1998). .

  2. 2.

    Aside from fostering opportunities for deviance, involvement in unstructured socializing may also affect deviant behavior through processes of socialization (Hoeben and Weerman 2016). Under this normative perspective, peer influence is also theorized to explain variation in behavior-specificity of individuals’ deviance. Transference of norms and values depends on the balance of definitions to which the individual is exposed. An excess of definitions favorable to violence may affect individuals’ own tolerance toward violence, but it does not necessarily affect their tolerance toward theft (Akers 1998; Jackson et al. 1986; Sutherland and Cressey 1955; Thomas 2015; Warr 2002).

  3. 3.

    Under the assumption that their prior experience was a positive one (Stafford and Warr 1993).

  4. 4.

    These arguments are in line with the unstructured socializing perspective as outlined by Haynie and Osgood (2005). They argue that an interaction between time spent unstructured socializing and having delinquent peers is consistent with the opportunity perspective, although such interaction is not required for explaining how involvement in unstructured socializing would increase risks for delinquency. That is, the general risk of deviance associated with time spent unstructured socializing is not contingent on the behavior of peers, but the deviance of the peers who are present in the setting can still heighten the risk for adolescents’ own deviance.

  5. 5.

    The answer categories for these questions did not include an option for ‘no friends from other grades or other schools’. Therefore, we treated missing values as zero. When missing values are excluded, respondents reported on average 6.1 friends in other grades in school and 5.4 friends from other schools.

  6. 6.

    Only differences with medium to large effect sizes (> 0.25) are reported here. The other statistically significant differences had very small effect sizes, suggesting that their significance was due to the large sample size.

  7. 7.

    We tried to retain as many respondents in the analyses as possible. This means we included individuals who had sufficient information for inclusion in some, but not all of the models. Any discrepancies between the maximum number of respondents and observations reported here and the number of respondents and observations in the final models (as reported in the tables) are due to this inclusion strategy.

  8. 8.

    In supplementary analyses, we also ran the models for only those respondents who reported at least two deviant acts (findings available from the first author).

  9. 9.

    For example, if an individual would report 2 types of theft, 1 type of vandalism, and no types of violence, their total number of reported behavior types is 3. This person would have a score of 0.67 on the theft specialization measure (2/3), a score of 0.33 on the vandalism specialization measure (1/3), and a score of 0 on the violence specialization measure (0/3).

  10. 10.

    The specialization measures differ from the Diversity Index that has been previously applied in individual-level research on offender versatility (e.g., Mazerolle et al. 2000; McGloin et al. 2007; Piquero et al. 1999; Sullivan et al. 2006) in two main respects. First, higher scores indicate specialization rather than versatility. Second, separate measures are constructed for each type of deviance, whereas the Diversity Index was developed to express versatility in one measure. The measures are similar to the Offense Specialization Coefficient employed by DeLisi et al. (2011), except that it was calculated for behavior categories (e.g., theft, vandalism) rather than individual items.

  11. 11.

    Two other possible approaches are to simply take the sum without correcting for the number of friends (the summative measure), or to divide the sum by the number of friends (the average measure). All models were replicated with average measures instead of the square root measures (findings available from the first author). The findings were fairly similar across measurement strategies, but the models using the square root measure fitted the data better than the models using the average measure. See “Appendix A” in the online supplementary material for a more detailed discussion of the different measures.

  12. 12.

    We also constructed several alternative measures including the proportion of best and stable friends who were deviant; the proportion of deviant friends who reciprocated the friendship nomination or were best or stable friends; and the proportion of all nominated friends who were deviant as well as best, stable, or reciprocal friends. Bivariate correlations indicated that of all examined measures, the control variable we used in the main analyses was the strongest predictor of each dependent variable, and thus offered the most conservative control.

  13. 13.

    Coefficients were interpreted as log linear and express the change in the log count of the outcome associated with every one-unit increase in the independent variable. The incidence rate ratio (IRR) expresses the multiplicative change in the outcome measure with every one-unit increase in the predictor.

  14. 14.

    Coefficients express the expected change in the log odds of the outcome with every one-unit increase in the independent variable. The odds ratio (OR) expresses the multiplicative change in the odds of the outcome measure with every one-unit increase in the predictor.

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Acknowledgements

The authors would like to thank Gerben Bruinsma, the editor, and the anonymous reviewers for their valuable comments and suggestions.

Funding

This work was supported by Grants from the W.T. Grant Foundation [8316]; National Institute on Drug Abuse [R01-DA018225]; and National Institute of Child Health and Human Development [R24-HD041025]. The analyses used data from PROSPER, a Project directed by R. L. Spoth, funded by the National Institute on Drug Abuse [R01-DA013709]; and the National Institute on Alcohol Abuse and Alcoholism [AA14702]. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Hoeben, E.M., Osgood, D.W., Siennick, S.E. et al. Hanging Out with the Wrong Crowd? The Role of Unstructured Socializing in Adolescents’ Specialization in Delinquency and Substance Use. J Quant Criminol 37, 141–177 (2021). https://doi.org/10.1007/s10940-019-09447-4

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Keywords

  • Delinquency versatility
  • Poly-substance use
  • Unstructured socializing
  • Peer influence
  • Adolescence