title: Predictive validity of pilot selection instruments for remotely piloted aircraft training outcome.
abstract: BACKGROUND
  Demand for remotely-piloted aircraft (RPA) support has increased dramatically over the last decade. Initial efforts to meet the demand focused on cross-training experienced manned aircraft pilots and funneling recent Specialized Undergraduate Pilot Training (SUPT) graduates to RPA pilot training. This approach reduced the number of personnel available for manned airframes and is no longer sustainable. In 2009, the USAF established an RPA career field and the Undergraduate RPA Training (URT) course to train officers with no prior flying experience to be RPA pilots. URT selection methods are very similar to those for SUPT. Some important factors for URT applicants are medical flight screening and aptitude tests [Air Force Officer Qualifying Test (AFOQT) and Pilot Candidate Selection Method (PCSM)]. The current study examined the predictive validity of the AFOQT pilot and PCSM composites for URT completion.
  
  
  METHODS
  Subjects were 139 URT students with AFOQT and PCSM scores. The training criterion was URT pass/fail and the pass rate was 74.8%.
  
  
  RESULTS
  Both the AFOQT pilot (r = 0.378) and PCSM (r = 0.480) composites demonstrated good predictive validity.
  
  
  CONCLUSIONS
  No minimum qualifying PCSM score exists for URT. Had a minimum PCSM score of 25 been used, the pass rate would have been 80.2%; 12 more eliminees would have been screened out compared with the current AFOQT pilot minimum qualifying score of 25. Although current selection methods are effective, based on results of several RPA job/task analyses, the Air Force is examining the utility of other measures to supplement current methods.
authors.affiliation: Air Force Research Laboratory, Wright-Patterson AFB, Area B, Bldg. 146, Rm. 122, 45433-7511 OH, USA. thomas.carretta@wpafb.af.mil
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Carretta
mesh_terms: D000401:Aircraft; D006801:Humans; D008328:Man-Machine Systems; D008887:Military Medicine; D008889:Military Personnel; D010555:Personality Inventory; D010560:Personnel Selection; D058998:Remote Sensing Technology; D013647:Task Performance and Analysis