Optimizing Prediction of Attrition With the U.S. Army's Assessment of Individual Motivation (AIM)

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.

ACKNOWLEDGMENTS

We express our gratitude to Dr. Michael G. Rumsey and the U.S. Army Research Institute for the Behavioral and Social Sciences for their help in this investigation. Assistance from the Human Resources Research Organization (HumRRO) and the Consortium of Universities of the Washington Metropolitan Area is also greatly appreciated.

Notes

The views expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the U.S. Department of Defense or any of its agencies, including the U.S. Army Research Institute, the U.S. Army, HumRRO, the Consortium of Universities, or Valtera. All work for this investigation was conducted when the first three authors were enrolled as doctoral students at the University of Illinois at Urbana–Champaign.