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Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
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, anddoi:10.25035/pad.2022.01.007 doaj:e3942aa18c274699a5a5c33a8f9a9ae3 fatcat:whfeqsuwpfhm5gmp2gcphxb3gu