Never going to let you down: Preventing predictive shrinkage via the STRONG-R assessment method

Abstract

Risk-needs assessments (RNAs) are an evidence-based practice used by practitioners to assign supervision and programming. While foundational to day-to-day practices, these tools are typically applied ‘off-the shelf,’ and are mistakenly assumed to demonstrate equivalent prediction accuracy regardless of location or population. Although researchers and providers are aware these tools experience performance shrinkage, the issue is commonly ignored. In 2016 the Tennessee Department of Correction (TDOC) collaborated with the developers of the Static Risk Offender Needs Guide – Revised (STRONG-R) to create a staged development of a locallydeveloped risk needs assessment. Using propensity score matching, a proxy sample was used to create an initial version of the STRONG-R, representing a TDOC-like sample. This version was deployed in 2017. Following data collection, developers recrafted the tool with local data to make Version 2.0. Findings demonstrate improved performance using this innovative method, effectively eliminating performance shrinkage for the TDOC STRONG-R.

Zachary Hamilton

Professor

My research centers on innovation in risk and needs assessment development.

Alex Kigerl

Research Associate

I am an RNA development expert, specializing in data management and analysis.

Baylee Allen-Flores

Ph.D. Candidate

My research interests are risk-needs assessments, implementation science, prison misconduct, and prison policy.

John Ursino

Ph.D. Candidate

My research focuses on advancing evidence-based practices in criminal justice. Primary areas of research include evaluating and constructing risk-need assessments to enhance decision-making processes, addressing challenges related to prison and jail crowding, and exploring correctional system reforms.