Examining Whether Practical Measures of Neighborhood Characteristics Improve Correctional Risk Assessment Tools

Abstract

This study examined whether inclusion of a neighborhood domain improved prediction and classification in an existing risk assessment tool. Logistic regression and analysis of covariance (ANCOVA) with random effects were conducted using a sample of individuals under community supervision ( N = 10,548) to determine whether a neighborhood domain improved the predictive validity of the Ohio Risk Assessment System-Community Supervision Tool (ORAS-CST). In five of our six models, inclusion of the neighborhood domain did not significantly improve the predictive validity of the ORAS-CST regardless of whether it was considered as an additive variable or moderator. One model found that the addition of a neighborhood domain improved prediction; however, the relationship was opposite from what was theoretically expected. The findings suggest that individual-level factors remain the most meaningful predictors in correctional risk assessment tools for those under community supervision. Future research is needed on whether neighborhood indicators improve assessment tools for individuals under other forms of supervision.