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AbstractThis article delves into the connections between time trends in unstructured socializing and other dramatic changes in adolescence since the 1950s. Osgood et al.'s (1996) individual‐level application of routine activity theory proposed that unstructured socializing contributes to crime by exposing people to situations conducive to deviance, and a large body of research supports this idea. Unstructured socializing has proven useful as an explanatory bridge that links crime and deviance to key social factors like age, class, and gender. The present article expands on two recent studies (Baumer et al., 2021; Svensson & Oberwittler, 2021), which showed that changing rates of unstructured socializing help explain time trends in delinquency as well. Based on time trends across many domains of adolescent life, I argue that changes in unstructured socializing were a manifestation of the rise and fall of a teen culture of independence from the 1950s to the 2020s. The effectiveness of unstructured socializing as an explanatory bridge demonstrates the value of focusing on ordinary, everyday activities for tapping into features of life that are consequential and that vary across social conditions.

This article reflects on the progress of research on developmental and life-course criminology, comments on the status of some unresolved issues, and offers recommendations for the future. The first sections relate these articles and the current status of the field to two themes from the criminal careers debate of the 1980s and 1990s: generalization versus disaggregation as approaches to advancing science and continuous versus categorical conceptions of variation in criminal careers. The article also discusses the use of the growth curve models that are so prominent in developmental and life-span research, emphasizing the aspects of change that they do and do not capture, pointing out implications of that limitation for the need for expanding theories andmodels of change, and explaining the simple steps needed to enhance growth curve models to accomplish that purpose.

This article reflects on the progress of research on developmental and life-course criminology, comments on the status of some unresolved issues, and offers recommendations for the future. The first sections relate these articles and the current status of the field to two themes from the criminal careers debate of the 1980s and 1990s: generalization versus disaggregation as approaches to advancing science and continuous versus categorical conceptions of variation in criminal careers. The article also discusses the use of the growth curve models that are so prominent in developmental and life-span research, emphasizing the aspects of change that they do and do not capture, pointing out implications of that limitation for the need for expanding theories and models of change, and explaining the simple steps needed to enhance growth curve models to accomplish that purpose. Figures, References. [Reprinted by permission of Sage Publications Inc., copyright 2005 The American Academy of Political and Social Science.]

For juvenile diversion programs to reach their goals of reducing labeling, social control, and costs, they must serve a population that is diverted from processing in the justice system. Some evaluations imply that these goals conflict with the goal of reducing recidivism because diversion programs are most effective with youths who have the least serious offense histories. The present study examines the issue using data for a broad range of outcomes from three programs that randomly assigned youths to treatment and control groups. Data analysis indicated no relationship between program effectiveness and the seriousness of clients' offense histories.

Using a large sample of men, we examine alternative models of the way in which testosterone may influence adult deviant behavior. The results indicate a significant and moderately strong relationship between testosterone and adult deviance, and this relationship between testosterone and adult deviance is largely mediated by the influence of testosterone on social integration and on prior involvement in juvenile delinquency. In addition, testosterone level moderates the relationship of social integration to adult deviance: The restraining influence of social integration is less necessary for men with lower levels of testosterone. Further, prior delinquency interacts with social integration in the same fashion, accounting for much of the moderating effect of testosterone. This pattern of results supports the conclusions that (1) testosterone is one of a larger constellation of factors contributing to a general latent propensity toward deviance and (2) the influence of testosterone on adult deviance is closely tied to social factors. Our findings show that there is considerable promise in a biosocial approach that integrates social and biological explanations, rather than playing them off against one another.

Companions are central to explanations of the risky nature of unstructured and unsupervised socializing, yet we know little about whom adolescents are with when hanging out. We examine predictors of how often friendship dyads hang out via multilevel analyses of longitudinal friendship‐level data on over 5,000 middle schoolers. Adolescents hang out most with their most available friends and their most generally similar friends, not with their most at‐risk or similarly at‐risk friends. These findings vary little by gender and wave. Together, the findings suggest that the risks of hanging out stem from the nature of hanging out as an activity, not the nature of adolescents' companions, and that hanging out is a context for friends' mutual reinforcement of preexisting characteristics.

Specialization in violence is an important scientific and policy topic, and over the past several decades, many analysis techniques for studying specialization have emerged. Research in this area continues to be hampered, however, by remaining methodological problems. To overcome these problems, we propose a new method for studying specialization in violence based on an item‐response theory measurement approach that is implemented through a multilevel regression model. Our approach defines specialization as an individual level latent variable, takes into account the inherent confounds between specialization and overall level of offending, and gauges specialization relative to the population base rates of each offense. Our method also enables researchers to 1) estimate the extent and statistical significance of specialization, 2) assess the stability of specialization over time, and 3) relate specialization to explanatory variables. Using data from three studies, we found substantial levels of specialization in violence, considerable stability in specialization over time, and several significant and relatively consistent relationships of specialization to explanatory variables such as gender, parental education, and risk‐seeking.

This article applies an individual‐level routine activities perspective to explaining rates of delinquency. The theoretical analysis also links the opportunity processes of that perspective to key themes of social disorganization theory. Multilevel analyses of 4, 358 eighth‐grade students from thirty‐six schools in ten cities support the central hypothesis: Time spent in unstructured socializing with peers has both individual and contextual effects that explain a large share of the variation in rates of delinquency across groups of adolescents who attend different schools. In addition, parental monitoring has a very strong contextual effect on unstructured socializing, which supports the proposed integration of routine activity and social disorganization perspectives.

In order to extend the study of community social disorganization and crime beyond its exclusive focus on large urban centers, we present an analysis of structural correlates of arrest rates for juvenile violence in 264 nonmetropolitan counties of four states. Findings support the generality of social disorganization theory: Juvenile violence was associated with rates of residential instability, family disruption, and ethnic heterogeneity. Though rates of poverty were not related to juvenile violence, this is also in accord with social disorganization theory because, unlike urban settings, poverty was negatively related to residential instability. Rates of juvenile violence varied markedly with population size through a curvilinear relationship in which counties with the smallest juvenile populations had exceptionally low arrest rates. Analyses used negative binomial regression (a variation of Poisson regression) because the small number of arrests in many counties meant that arrest rates would be ill suited to least‐squares regression.

Longitudinal research designs with many waves of data have the potential to provide a fine-grained description of program impact, so they should be of special value for evaluation research. This potential has been illusive because our principal analysis methods are poorly suited to the task. We present strategies for analyzing these designs using hierarchical linear modeling (HLM). The basic growth curve models found in most longitudinal applications of HLM are not well suited to program evaluation, so we develop more appropriate alternatives. Our approach defines well-focused parameters that yield meaningful effect-size estimates and significance tests, efficiently combining all waves of data available for each subject. These methods do not require a uniform set of observations from all respondents. The Boys Town Follow-Up Study, an exceptionally rich but complex data set, is used to illustrate our approach.