Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis

In employee selection and academic admission decisions, holistic (clinical) data combination methods continue to be relied upon and preferred by practitioners in our field. This meta-analysis examined and compared the relative predictive power of mechanical methods versus holistic methods in predicting multiple work (advancement, supervisory ratings of performance, and training performance) and academic (grade point average) criteria. There was consistent and substantial loss of validity when data were combined holistically-even by experts who are knowledgeable about the jobs and organizations in question-across multiple criteria in work and academic settings. In predicting job performance, the difference between the validity of mechanical and holistic data combination methods translated into an improvement in prediction of more than 50%. Implications for evidence-based practice are discussed.