Identifying and Validating Selection Tools for Predicting Officer Performance and Retention

Abstract : The U.S. Army must commission officers who are likely to perform well as junior officers, fit into the Army's culture, demonstrate leadership potential for higher ranks, and be motivated to stay beyond their initial Active Duty Service Obligation (ADSO). To address this requirement, the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) has undertaken a program of research to enhance officer selection, assignment, and retention. The primary purpose of this effort was to evaluate a number of predictor measures against officer performance and career intentions. The core activity was a concurrent, criterion-related validation project in which several predictor and criterion measures were administered to over 800 early- and mid-career officers. Results showed that a number of measures were useful predictors of officer performance and retention. The Rational Biodata Inventory (RBI), the Leader Knowledge Test (LKT), the Objective-Format Consequences Test, and College GPA were good predictors of technical, managerial, leadership, and effort/discipline performance dimensions.

[1]  S. Richardson,et al.  Variable selection and Bayesian model averaging in case‐control studies , 2001, Statistics in medicine.

[2]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[3]  P. Lewis Career Path Appreciation (CPA) Data Reduction and Analysis , 1993 .

[4]  Andrea L. Sinclair,et al.  Delineating Officer Performance and Its Determinants , 2014 .

[5]  Dan J. Putka,et al.  Identifying the Leaders of Tomorrow: Validating Predictors of Leader Performance , 2014 .

[6]  Tiffany Smith,et al.  The Use of Objective Measures as Criteria in I/O Psychology , 2012 .

[7]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[8]  Elliott Jaques,et al.  Development of Stratified Systems Theory for Possible Implementation in the U.S. Army , 1990 .

[9]  John P. Campbell,et al.  Behavior, Performance, and Effectiveness in the Twenty-first Century , 2012 .

[10]  Dan J. Putka,et al.  Scoring Situational Judgment Tests Using Profile Similarity Metrics , 2010 .

[11]  S. Zaccaro,et al.  Officer Individual Differences: Predicting Long-Term Continuance and Performance in the U.S. Army , 2012 .

[12]  A. Raftery Bayesian Model Selection in Social Research , 1995 .

[13]  T. Tremble,et al.  Development and Validation of Measures for Selecting Soldiers for the Officer Candidate School , 2011 .

[14]  Casey Wardynski,et al.  Towards a U.S. Army Officer Corps Strategy for Success: A Proposed Human Capital Model Focused upon Talent , 2012 .

[15]  J. W. Johnson A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression , 2000, Multivariate behavioral research.

[16]  Teresa L. Russell,et al.  Army Officer Job Analysis: Identifying Performance Requirements to Inform Officer Selection and Assignment , 2011 .

[17]  Charles A. Scherbaum,et al.  Validation Is Like Motor Oil: Synthetic Is Better , 2010, Industrial and Organizational Psychology.

[18]  H. Gulliksen Theory of mental tests , 1952 .

[19]  Deirdre J. Knapp,et al.  Project a 12 years of R & D , 2010 .

[20]  Craig K. Enders,et al.  The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models , 2001 .

[21]  Mathew T Allen,et al.  Selecting Soldiers and Civilians into the U.S. Army Officer Candidate School : Developing Empirical Selection Composites , 2014 .

[22]  Bengt Muthén,et al.  On structural equation modeling with data that are not missing completely at random , 1987 .

[23]  Longitudinal Validation of Non-Cognitive Officer Selection Measures for the U.S. Army Officer Candidate School (OCS) , 2012 .

[24]  P. Roth MISSING DATA: A CONCEPTUAL REVIEW FOR APPLIED PSYCHOLOGISTS , 1994 .

[25]  Craig K. Enders,et al.  The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data , 2001 .