Most of the empirical research and practically all of the fieldwork conducted on gangs has been devoted to street gangs. In this article, Bureau of Prisons automated data were used to evaluate the contribution of prison gang affiliation to violence and other forms of misconduct within prisons. The authors also examined a measure of gang embeddedness to see if, similar to street gang research, it can be shown that core members of a prison gang were more likely to commit violent and other kinds of misconduct than were more peripheral members. Both specific and more generic gang indicators were related to violence and other forms of official prison misconduct. A composite measure of gang misconduct represents the threat that particular gangs pose to prison order. The “threat index” is model based and provides a graphical representation of the relative magnitude and heterogeneity of the threat posed by different gang affiliations.

Without controlling for selection bias, the effects of treatment would most likely have been attenuated. The results have greater generalizability than other studies of prison-based treatment. This study occurred within a multisite context of 20 programs serving both male and female inmates and operating within different security levels and different geographic regions.

In an evaluation of prison-based residential drug treatment programs, the authors use three different regression-based approaches to estimating treatment effects. Two of the approaches, the instrumental variable and the Heckman approach, attempt to minimize selection bias as an explanation for treatment outcomes. Estimates from these approaches are compared with estimates from a regression in which treatment is represented by a dummy variable. The article discusses the advantage of using more than one method to increase confidence in findings when possible selection bias is a concern. Three-year outcome data for 2,315 federal inmates are used in analyses where the authors separately examine criminal recidivism and relapse to drug use for men and women. Statistical tests lead the authors to conclude that treatment reduces criminal recidivism and relapse to drug use. The treatment effect was largest when the inference was based on the Heckman approach, somewhat smaller when based on the instrumental variable approach, and smallest when based on the traditional dummy variable approach. Treatment effects for females were not statistically significant.

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