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In this project, we selected "best bet" predictor measures to help the U.S. Air Force (USAF) identify early career officers and airmen likely to succeed as a Remotely-Piloted Aircraft (RPA) Pilot (officer) or Sensor Operator (enlisted). We compiled existing information about skills, abilities, and other characteristics (SAOCs) predictive of success in RPA Pilot or SO training and information about the context in which this work is performed. We organized the SAOCs from diverse sources according to the U.S. Department of Labor's O*NET content model to minimize redundancy across constructs and to ensure broad coverage of several different domains of individual differences. Ultimately, 21 critical SAOCs were identified, most of which can be adequately measured with assessments already used by or accessible to the USAF. After considering practical constraints on the entry-level selection process for officers and airmen, we recommended two possible batteries of predictor measures for each position (Pilot and Sensor Operator). Finally, we addressed measurement gaps by developing a measure of time-sharing ability that does not couple cognitive processing and psychomotor tasks and a Person-Environment (P-E) fit measure customized for the RPA work context. The new measures require further evaluation before they can be used to make operational decisions.

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