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The Air Force Officer Qualifying Test (AFOQT) is used to qualify applicants for officer commissioning and for aircrew training. Although the current aircrew aptitude composites have shown predictive validity against initial aircrew training outcomes for many years, they also have demonstrated moderate to large mean score subgroup differences (SGDs) for females and racial/ethnic minorities. Historically, AFOQT aptitude composites have been computed from a combination of the cognitive subtests. The current study examined the utility of Predictive Success Models (PSMs) which added personality facets from the Self-Description Inventory for Officers to the existing cognitive composites. Three statistical methods were utilized to create new PSMs: Nonlinear Multiple Regression, Corrected Linear Multiple Regression, and Corrected Pareto Optimization. The best performing models created from each method were tested against each other, and against the current cognitive composites. The new models were found to be successful in increasing criterion-related validity and maintaining or decreasing SGDs.