Optimizing prediction of attrition with the U.S.Army's assessment of individual motivation (AIM)

Stephen Stark, Oleksandr S. Chernyshenko, Fritz Drasgow, Wayne C. Lee, Leonard A. White, Mark C. Young

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)180-201
Number of pages22
JournalMilitary Psychology
Volume23
Issue number2
DOIs
StatePublished - Mar 1 2011

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Social Sciences (miscellaneous)
  • Psychology(all)

Fingerprint Dive into the research topics of 'Optimizing prediction of attrition with the U.S.Army's assessment of individual motivation (AIM)'. Together they form a unique fingerprint.

  • Cite this