![]() With an increase in score cutoff correlating to a pass rate of 88% (with more test takers since there are more programs), then 1,140 test takers fail.Īccording to ARC-PA, there are 242 accredited PA programs. If 9,000 people took the PANCE in 2018 and the pass rate was 95%, then 450 test takers failed. So…if I barely passed the PANCE back in January, that same score today would be a failure. It appears that the passing cutoff changed. However, if I take the PANCE today and score a 350…I fail.Īccording to the announcements from the NCCPA, there is no indication that question difficulty changed. For example, if I took the PANCE in January 2019 and scored a 350 (let’s say that roughly correlated to a percentage score of 70%), I’d pass. How they are altering is still unclear, but history tells us they are increasing the passing cutoff score. The NCCPA announced they are altering the scoring of the PANCE. Throughout NCCPA’s history, although an increased passing standard typically results in a slightly reduced passing rate, over time the passing rate steadily increases. Based on the analyses conducted to estimate performance on the PANCE, it is anticipated that the pass rate for the 2019 PANCE will be slightly lower than the past few years. This trend is fairly typical of the historical standard settings. This indicates that the group of PAs who participated in the study agreed that entry-level PAs should demonstrate a slightly increased level of content knowledge in order to achieve initial certification. ![]() The recommendation from the 2018 PANCE standard setting study was approved by the NCCPA Board of Directors and resulted in a passing standard with an anticipated slightly lower passing rate when compared to the last several years. Scores typically decline for a while but gradually improve once the blueprint changes are digested and the test-taking community gets a better grasp of what new material appeared on the exam.īut wait, there’s more… NCCPA Announces Expected Decreased PANCE Pass Rates: One of the consequences of altering a blueprint is often a more challenging exam. There were significant changes in this update. Future research should examine which specific noncognitive traits measured in interviews can add value to predictability.Back in January 2019, the NCCPA released an updated Content Blueprint. Years of health care experience, grades on prerequisites, and demographics were not significant predictors across programs but did have significance in certain individual institutions. Each of these four predictors can be plugged into predictability tables to estimate the probability of achieving various score intervals on the PANCE.Ī model of equations and predictors can be used to project how successful a physician assistant (PA) graduate will be on PANCE performance. The PACKRAT scores were consistently the best predictors of performance on the PANCE. Expectancy tables were developed to provide estimation of PANCE performance, given the various score ranges on each of the predictor variables.įour predictors made a significant contribution to the final regression equation: GPA, GRE-verbal, GRE-quantitative, and PACKRAT scores. Multiple regression analysis was used to develop prediction equations. While PACKRAT scores are not applicable to admission selection, they are a strong midpoint predictor of PANCE performance. ![]() 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). 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).Ī 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.
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