Using Regression Analysis to Find Predictors of Performance in a Pharmacotherapy Course

OBJECTIVE: Examine variables internal and external to the classroom and their relationship with final course outcomes within a Doctor of Pharmacy (PharmD) program using exploratory linear regression analysis. METHODS: Data were collected from students enrolled in a required Pharmacotherapy I course in fall 2023. An exploratory linear regression model was generated using eight variables. Variables included in the final model were derived from PharmD admissions data, course learning analytics, and a voluntary survey administered in the course about student study habits. RESULTS: A complete data set was collected from 57 out of 98 students (58%). Correlations among variables revealed final course grade was positively associated with sense of belonging (r=.544), GPA at time of matriculation (r=.537), spending 15 or more hours on co-curriculars in a month (r=.421), and average practice quiz score (r=.214). Final course grade was negatively correlated with an Early Warning and Intervention (EWI) policy triggered (r=-.652), progression committee attendance (as related to other courses) (r=-.553), attending office hours to study for an exam (r=-.401) (open to all, but required for students who trigger the EWI policy), and usage of recorded lectures (r=-.377). The revised regression model is statistically significant (F(8, 48)=16.763, p<.001) CONCLUSION: A combination of factors matter in determining final course grades, including those directly connected to the class and those outside of it. A similar approach could be replicated by others interested in exploring influences on learning outcomes in their own courses.