title: Admission variables as predictors of PANCE scores in physician assistant programs: a comparison study across universities.
abstract: OBJECTIVE
  The purpose of this study was to create a model of cognitive and noncognitive measures that could estimate the probability of achieving a given level of performance on the Physician Assistant National Certifying Examination (PANCE).
  
  
  METHODS
  A retrospective records review of admissions information used by six universities was conducted to discover which factor had the most impact on the dependent variable of the PANCE score. Multiple predictors were measured: undergraduate grade point average (uGPA), graduate GPA, prerequisite grades, Graduate Record Exam (GRE)-verbal, GRE-quantitative, GRE combined, interview scores, years of health care experience, age, gender, and first-year scores on the Physician Assistant Clinical Knowledge Rating and Assessment Tool (PACKRAT). While PACKRAT scores are not applicable to admission selection, they are a strong midpoint predictor of PANCE performance. Multiple regression analysis was used to develop prediction equations. Expectancy tables were developed to provide estimation of PANCE performance, given the various score ranges on each of the predictor variables.
  
  
  RESULTS
  Four predictors made a significant contribution to the final regression equation: GPA, GRE-verbal, GRE-quantitative, and PACKRAT scores. The PACKRAT scores were consistently the best predictors of performance on the PANCE. Each of these four predictors can be plugged into predictability tables to estimate the probability of achieving various score intervals on the PANCE.
  
  
  CONCLUSIONS
  A model of equations and predictors can be used to project how successful a physician assistant (PA) graduate will be on PANCE performance. Years of health care experience, grades on prerequisites, and demographics were not significant predictors across programs but did have significance in certain individual institutions. Future research should examine which specific noncognitive traits measured in interviews can add value to predictability.
authors.affiliation: Physician Assistant Program, University of Detroit Mercy, Michigan 48221, USA.
authors.forename: Rose
authors.initials: R
authors.lastname: Higgins
authors.forename: Sharon
authors.initials: S
authors.lastname: Moser
authors.forename: Amy
authors.initials: A
authors.lastname: Dereczyk
authors.forename: Roberto
authors.initials: R
authors.lastname: Canales
authors.forename: Gloria
authors.initials: G
authors.lastname: Stewart
authors.forename: Colleen
authors.initials: C
authors.lastname: Schierholtz
authors.forename: Ted J
authors.initials: TJ
authors.lastname: Ruback
authors.forename: Jane
authors.initials: J
authors.lastname: McDaniel
authors.forename: James
authors.initials: J
authors.lastname: Van Rhee
authors.forename: Steve
authors.initials: S
authors.lastname: Arbuckle
mesh_terms: D004521:Educational Measurement; D006801:Humans; D010823:Physician Assistants / Q000193:education* / Q000592:standards*; D012044:Regression Analysis; D012189:Retrospective Studies; D012570:School Admission Criteria; D013647:Task Performance and Analysis; D014495:Universities