Social and Behavioral Sciences Commons^™

Comparing Empirically Keyed And Random Forest Scoring Models In Biodata Assessments, Mathijs Affourtit, Kristin S. Allen, Craig M. Reddock, Paul M. Fursman

Personnel Assessment and Decisions

Effective pre-hire assessments impact organizational outcomes. Recent developments in machine learning provide an opportunity for practitioners to improve upon existing scoring methods. This study compares the effectiveness of an empirically keyed scoring model with a machine learning, random forest model approach in a biodata assessment. Data was collected across two organizations. The data from the first sample (N=1,410), was used to train the model using sample sizes of 100, 300, 500, and 1,000 cases, whereas data from the second organization (N=524) was used as an external benchmark only. When using a random forest model, predictive validity …