How to predict recidivism in young offenders: comparing logistic regression, random forest, and boosted classification trees and examining possible risk factors

Abstract

One important goal of a young offender institution in Germany is to encourage the young offenders (YO) to not commit another crime in the future. In order to enhance the effectiveness of interventions that aim at this goal, research is needed concerning the question under what circumstances a YO recidivates. Traditionally, logistic regression is used in order to analyze recidivism data. Here it is argued that the use of tree-based statistical methods might yield new insights for the prediction of recidivism. It is hypothesized that boosted classification trees yield better predictions than random forest, and that random forests yield better predictions than logistic regression. This study involved 643 YO that were released from the German young offender institution Regis-Breitingen. Predictors included in this study were demographic information, the interventions a YO participated in, an assessment by professionals and a self-assessment. The outcome criterion was whether or not the YO received a new court decision (except for acquittal) within two years after release. The results showed that there was no difference in predictive performance between the methods. All methods performed poorly. Static risk factors for recidivism were younger age and a shorter amount of time spent in the young offender institution. Dynamic risk factors for recidivism included the YO having no place in work, vocational training or school after release, having a need to continue structured transition management after release as well as having participated in delict- or problem specific measures. A possible reason for poor predictive performance is heterogeneity of the YO. Implications for further research and policy making are discussed.