Using Departmental Indicators and Machine Learning to Predict Early-career Patrol Officer Retention

• In Event: Policing and Technology

Abstract

Police departments are facing a historic crisis in recruiting and retaining officers. While researchers have identified numerous factors that influence officers’ decisions to stay or leave their position – including their relationship with their supervisor, compensation, burnout, and work stress – most are subjective and can only be collected qualitatively through conversations and interviews or surveys. To address this gap, the current study employs a data-driven machine-learning approach using indicators from department-collected administrative records to predict officer retention for patrol officers in their first three years on the job. We examine 1,164 new hires in the Phoenix Police Department between 2015 and 2020, with 18% having left their role in the same time period. Early identification of officers at risk of leaving may help departments address individual workplace concerns and keep good officers on the job.

Authors

• Kayla Freemon, DePaul University

• Charles Katz, Arizona State University

• Hyunjung Cheon, University of Texas at El Paso

• Danielle Wallace, Arizona State University

• Jessica Huff, University of Cincinnati