Optimizing Prediction of Attrition With the U.S. Army's Assessment of Individual Motivation (AIM) |
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| Authors: | Stephen Stark Oleksandr S. Chernyshenko Fritz Drasgow Wayne C. Lee Leonard A. White Mark C. Young |
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| Affiliation: | 1. University of South Florida , Tampa , Florida sestark@usf.edu;3. Nanyang Technological University , Singapore;4. University of Illinois at Urbana–Champaign , Champaign , Illinois;5. Valtera , Chicago , Illinois;6. U.S. Army Research Institute , Washington , District of Columbia |
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| Abstract: | The regression framework is often the method of choice used by psychologists for predicting organizationally relevant outcomes from test scores. However, alternatives to regression exist, and these techniques may provide better prediction of outcomes and a more effective means of classifying examinees for selection and placement. This research describes two of these alternatives—decision tree methodology and optimal appropriateness measurement (OAM)—and how they were used to optimize the prediction of attrition among a sample of first-term enlisted soldiers (N?=?22,537) using a temperament inventory called the Assessment of Individual Motivation (AIM). Results demonstrated that the OAM approach provided better differentiation between “stayers” and “leavers” after 12 months than either the traditional logistic regression or the decision tree methods. |
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