Nevin Manimala Statistics

Path Analysis of Admission and Program Achievement Variables to Predict Physician Assistant National Certifying Examination Performance: Results of a 6-Year Study

J Physician Assist Educ. 2023 Dec 13. doi: 10.1097/JPA.0000000000000567. Online ahead of print.


PURPOSE: The primary aim of this study was the examination of relationships between students’ preadmission achievement, intraphysician assistant (PA) program achievement, and Physician Assistant National Certifying Examination (PANCE) performance using path analysis regression. Second, this study explored the extent to which the theoretical model differed based on several key demographic variables: sex and undergraduate major.

METHODS: This retrospective, single-institution study examined data from 2015 to 2022 (n = 322). Analysis included descriptive statistics, bivariate correlations, and path analysis using structural equation modeling to examine direct, indirect, and total effects of all predictors on the primary outcome variable, PANCE.

RESULTS: PACKRAT-I demonstrated the largest total effect size on PANCE total score (β = .45). Total effect size on PANCE was small yet significant for prerequisite grade point average (GPA), Graduate Record Exam verbal and quantitative subscores, a comprehensive didactic cardiology examination, didactic and clinical year GPAs, and End of Rotation examination mean score (β < .25). The relationship between mean preceptor evaluation score and PANCE was nonsignificant. Subgroup analyses showed differences between female and male in the relationship between several didactic variables and preceptor evaluations. No differences were detected between groups based on undergraduate major.

CONCLUSION: This PANCE analysis revealed relationships among pre-PA and intra-PA performance metrics that may subsequently support data-informed strategies for programs to identify at-risk students, aid student success, and support the assessment of curriculum. Future studies should replicate the approach using a larger, multi-institution sample that examines additional preprogram and intraprogram achievement variables.

PMID:38091357 | DOI:10.1097/JPA.0000000000000567

By Nevin Manimala

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