Code in Transition? The Evolution of Code of the Street Adherence in Adolescence

ABSTRACT

Using four waves (n = 2,385) from a student sample drawn in large U.S. cities, we examine the code of the streets’ influence on criminal offending and conflict management. Key to the analysis is the theoretical notion that effects are most pronounced for those who believe in the code intractably. We perform Latent Class Analysis to identify adherence types and use Latent Transition Analysis to measure the individual change in street-code class membership. Findings reveal four classes, distinguished mainly by the level of agreement. Those high and stable on the code are more likely to engage in crime and have diminished conflict management skills.

Notes

¹ The data used in this paper was supported by Award No. 2006-JV-FX-0011, National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this article are those of the current authors and do not necessarily reflect the views of the Department of Justice, the seven participating school districts, or of investigators on the project. The current article is a secondary analysis of a publicly archived data set.

² The data analysis was replicated and validated using [SAS/STAT] software, Version [8] of the SAS System for [Unix]. Copyright © SAS Institute Inc. SAS and other SAS Institute Inc. product or service names are registered trademarks of SAS Institute Inc., Cary, NC, USA.

³ We tested the class structure for modeling sensitivity, by also estimating a pooled model from waves 3–5, confirming the 4-class solution and classes with similar properties (results available on request). In addition, we confirmed this result in wave 6 (results available on request) and in a model where we recoded the items (0–1) for above and below neutral to examine sensitivity to thresholds (Appendix B).

⁴ A consistent finding in this data is that there is a great deal of temporal stability in street codes as can be seen in other data as well (Moule et al. 2015). This is not to say that there is not changed and developmental relationships in the data (Tables 3 and 4). For brief illustration, Appendix C. shows a cross-lagged model of a dichotomous street code indicator (0 = Class 1 or 2; 1 = 3 or 4) and the commission of a violent crime (0 = none, 1 = 1 or more). Not surprisingly, the largest effects are the lagged variables on the same variables in a subsequent wave. The lagged street code variable increases the odds of being in the same category by 27–28 times from wave 3 to 4 to 5. Committing a violent crime increases the odds of committing one in the subsequent wave by 4.5 to 7.1 times. Being in a high as opposed to low street code class doubles the odds of a violent crime in the next wave, whereas being violent increases the odds of being a member of one of the higher street code class subsequently by 1.4 times. In further exploration of developmental effects, we created variables for those who had shifted their class membership upward and downward (ex:0 = same or downward from class 3 or 4 to 1 or 2; 1 = shift from class 1 or 2 to 3 or 4). Increasing age, being a minority, and low self-control and a violent crime in the previous wave all increase the odds of shifting from class 1 or 2 to class 3 or 4 significantly and also operate in the counter direction on downward shifts. The largest effect was being a minority, which tripled the odds of shifting upward. These exploratory results reinforce the importance of these control variables and reiterate that amidst a great deal of stability, developmental variables are having effects. They also mirror results that are presented where we examine predictors of staying in each of the latent classes across waves.

⁵ The last model in the columns is very conservative as it contains general class, stayer class measures, general stayer and a lagged dependent variable. The lagged variable alters the analysis into an analysis of change. We did this at a reviewer’s request even though it is acceptable to look at distal outcomes as theoretical validation of class measures without examining change (Nyand et al. 2014).

⁶ We also modeled interaction effects between class membership and the mover-stayer variable for violent and any crime. Results indicate whether stayer effects differ by class, with class 2 stayer effects as the reference. Being a stayer in class 4 lead to a 2.7 times greater chance of being a property offender and a 2.3 times greater chance of being a violent offender. Both of these results fail to achieve significance with the full set of controls, however. We do not include the modeling of interaction effects for crime in Table 6 (available on request).

⁷ As was done for the analysis described above, we modeled interaction effects of class membership by the mover-stayer variable when predicting conflict management. Being a stayer in class 4 is the only interaction term that had significant effects. However, this effect loses significance after the addition of the full set of controls. Again, we do not include the interaction effects in Table 7, though results are available on request.

Additional information

Notes on contributors

Jacob H. Erickson

Jacob H. Erickson is a Ph.D. candidate in the Department of Sociology at Iowa State University. His research interests are in deviant identity and decision making processes. His dissertation explores motivations for using and selling drugs. He uses both quantitative and qualitative methods.

Andy Hochstetler

Andy Hochstetler is Professor in the Department of Sociology at Iowa State University. Much of his work examines offender decision-making and outcomes and incorporates offenders’ self-appraisals, estimated prospects for conventional success, and environmental contexts into understanding and predicting recidivism and crime continuation.

Shawn F. Dorius

Shawn F. Dorius is assistant Professor of Sociology at Iowa State University, where he conducts research in the sociology of culture and social demography. He has investigated worldwide patterns of change in mass education, human reproduction, and gender inequality. He has also conducted research on the measurement of inequality, cohort effects, and latent variables in multi-group contexts.