Prediction Modeling for Academic Success in Professional Master's Athletic Training Programs

Abstract

Context: A common goal of professional education programs is to recruit the students best suited for the professional career. Selection of students can be a difficult process, especially if the number of qualified candidates exceeds the number of available positions. The ability to predict academic success in any profession has been a challenging proposition. No studies to date have examined admission predictors of professional master's athletic training programs (PMATP).Objective: The purpose of this study was to identify program applicant characteristics that are most likely to predict academic success within a PMATP.Design: Cohort-based.Setting: University professional PMATP.Patients or Other Participants: A cohort of 119 students who attended a PMATP for at least 1 year.Intervention(s): Common application data from subjects' applications to the university and the PMATP were gathered and used to create the prediction models.Main Outcome Measure(s): Sensitivity, specificity, odds ratio, and relative frequency of success were used to determine the strongest set of predictors.Results: Multiple logistic regression analyses yielded a 3-factor model for prediction of success in the PMATP (undergraduate grade point average ≥ 3.18; Graduate Record Examination quantitative [percentile rank] ≥ 141.5 [≥12]; taking calculus as an undergraduate). A student with ≥2 predictors had an odds ratio of 17.94 and a relative frequency of success of 2.13 for being successful in the PMATP. This model correctly predicted 90.5% of PMATP success.Conclusions: It is possible to predict academic success in a PMATP based on common application data.