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.

Application patterns when applicants know the odds: implications for selection research and practice.

Unlike previous research that found small differences between population standard deviations and applicant pool standard deviations (P. R. Sackett & D. J. Ostgaard, 1994; D. S. Ones & C. Viswesvaran, 2003), this study revealed a 23% disparity between Law School Admission Test (LSAT) scores of all LSAT test takers and those of LSAT test takers who applied to law school. This study also illustrated robust applicant self-selection behavior across different law school ranks. These findings are important, because predictor scores of applicants who know their scores in advance and perceive small selection ratios necessitate substantially smaller range restriction corrections than those that would be required by population standard deviations. Furthermore, these findings more generally reveal that applicants who know their scores in advance behave quite differently from applicants who do not.