Moser, Sharon, Rose Higgins, and Amy Dereczyk
Purpose: The purpose of this study was to create a model of cognitive and non-cognitive measures that could more completely predict successful performance on the Physician Assistant National Certifying Exam (PANCE).
Methods: A retrospective records review of admissions information used by six universities was conducted to discover which source had the most impact on the dependent variable of the Physician Assistant National Certifying Exam (PANCE) score. Multiple predictors were measured: uGPA, graduate GPA, prerequisite grades, GRE-verbal, GRE-Math, GRE combined, interview scores, years of healthcare experience, age, gender and physician assistant knowledge rating and assessment tool (PACKRAT) first year scores. 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-Math 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.
Conclusion: A model of equations and predictors can be used to project how successful a Physician Assistant graduate will be on PANCE performance. Years of healthcare experience, grades on prerequisites and demographics were not significant predictors across the board but did have significance in some institutions. Future research should examine which specific noncognitive traits measured in interviews can add value to predictability.