After nearly four decades of growth, the number of people held in U.S. prisons has begun to decline. In an era of decarceration, social scientists need to understand prisoner reentry experiences. Longitudinal studies are one strategy to accomplish this goal. Yet, the retention of a formerly incarcerated population across waves of interviews is challenging due to their transient lifestyles and limited support systems, which may be further complicated by gang involvement. This article details the longitudinal follow-up procedures used in the LoneStar Project—a multiwave study of 802 males first interviewed in prison and reinterviewed twice in the year postrelease—to build rapport, complete interviews, and minimize attrition. We then evaluate the effectiveness of our procedures on important outcomes including interview yields, appointments, and incoming calls. Results indicate that any outgoing contact with respondents via appointment reminders and other reciprocal modes of contact lead to greater project engagement and a greater likelihood of interview completion. We conclude with relevant takeaways for researchers seeking to maximize survey participation with hard-to-reach populations.

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Longitudinal data are essential to research in criminology and criminal justice. Despite attrition’s implications for validity, understanding its sources is underexplored empirically. We examine the correlates of retention using covariates organized into domains of prediction, prevention, and projection. Data from the LoneStar Project, a three-wave longitudinal reentry study of 802 males recently released from prisoners in Texas, were analyzed to examine the correlates of proximal, distal, and any study retention. The best correlates of study retention are prevention techniques used by researchers to reduce attrition. In contrast, only a few covariates traditionally associated with attrition and no covariates used for attrition projection were related to retention. What researchers do matters more for retention than the characteristics of individuals they are trying to retain. The findings underscore how researchers can improve study retention in longitudinal research while also correcting for non-random attrition in current longitudinal data sources.

Objectives: Reentry experiences for the 600,000 people released annually from federal and state prisons differ vastly. We contend that gangs, which rose to prominence alongside mass incarceration, are an overlooked source of variation in reentry experiences. Drawing on precepts from the street gang literature, we test whether patterns of recidivism differ by official and survey measures of current, former, and non-gang status. Methods: Data from a representative sample of 802 prisoners interviewed prior to their release in 2016 were linked to 36 months of post-release arrest, conviction, and imprisonment records. Survival curves and multivariable discrete-time survival analysis were used to test for differential patterns of recidivism. Results: The conditional risk of recidivism varied by gang status. Current gang members maintained the greatest risk for all recidivism types. While former gang members were more likely to get arrested than non-gang members, there were no differences in conviction and imprisonment. Official and survey gang measures mostly told the same story, although official measures appeared to be more reliable determinants of recidivism than survey measures. Conclusions: Distinguishing former from current and non-gang members is important for policy, practice, and research. These findings renew calls to understand and respond to social groups and networks like gangs for prisoner reentry.

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