The Impact of Institutional Features on Student Retention Rates Using Regression and Random Forest Modeling

Student retention is a focus for higher education institutions aiming to improve student outcomes and institutional success. While previous research has often relied on qualitative assessments of college related factors, this project applies quantitative techniques at a national scale. Random forest and beta regression models were used to predict retention rates for public colleges based on institutional characteristics such as financial variables, enrollment patterns, and demographic metrics. The random forest models demonstrated higher accuracy than the beta regression models, leading us to find that financial variables and student integration factors are significant predictors of retention. Beta regression models, though less accurate, suggested demographics and overall enrollment were influential in predicting retention. These findings align with and expand upon existing retention theories, offering numerical evidence to support both modern and foundational retention theories.