Predicting Sexual Recidivism. (June 2020)

• Record Type: Journal Article
• Authors: Ozkan, Turgut; Clipper, Stephen J.; Piquero, Alex R.; Baglivio, Michael; Wolff, Kevin
• Other Names: Jennings Wesley G. guest-editor.
• Is Part Of: Sexual abuse. Volume 32:Number 4(2020)
• Page Start: 375
• Page End: 399
• Publication Date: 2020-06
• Subjects: sexual recidivism -- machine learning -- juvenile sex offender recidivism -- random forests; Sex offenders -- Rehabilitation -- Periodicals; Sex offenders -- Psychology -- Periodicals; 616.8583
• DOI: 10.1177/1079063219852944
• Languages: English
• ISSNs: 1079-0632

Abstract

The current study focuses on adolescents with sex offense histories and examines sexual reoffending patterns within 2 years of a prior sex offense. We employed inductive statistical models using archival official records maintained by the Florida Department of Juvenile Justice (FDJJ), which provides social, offense, placement, and risk assessment history data for all youth referred for delinquent behavior. The predictive accuracy of the random forest models is tested using receiver operator characteristic (ROC) curves, the area under the curve (AUC), and precision/recall plots. The strongest predictor of sexual recidivism was the number of prior felony and misdemeanor sex offenses. The AUC values range between 0.71 and 0.65, suggesting modest predictive accuracy of the models presented. These results parallel the existing literature on sexual recidivism and highlight the challenges associated with predicting sex offense recidivism. Furthermore, results inform risk assessment literature by testing various factors recorded by an official institution.