Improving Measurement and Prediction in Personnel Selection

This original article presents a collection of studies focusing on the application of machine learning (ML) to enhance measurement and prediction in personnel selection. The document outlines how ML can address historical inefficiencies in scoring narrative data, thereby improving the selection process. The studies demonstrate that ML algorithms can score narrative responses as accurately and reliably as human judges while increasing efficiency. The article discusses various studies, including one that examines the generalizability of algorithms across different prompts, and others that show how ML can complement traditional assessments by scoring open-ended questions. Additionally, it highlights the capability of ML to predict multiple outcomes simultaneously and improve job analysis efficiency. The findings suggest that ML has the potential to significantly impact personnel selection practices and methodologies moving forward.