Prediction of screening decisions in a medical school admission process

To study and predict a set of screening decisions in a medical school admission process, a stratified sample (n = 864) was drawn to represent the range of applicants to the Yale University School of Medicine during a single year. A smaller sample from the following year's applicants was drawn in a similar fashion for purposes of cross-validation. Prior to the prediction of screening decisions, a set of independent variables was selected by a factor analytic procedure from credentials in an applicant's admission folder. These folder variables ranged in nature from quantitative measures of academic performance to demographic information and types of extracurricular activity. Two multivariate statistical procedures, Sonquist's Automatic Interaction Detection (AID) and linear discriminant analysis (LDA), were used to predict screening decisions. Measures of academic performance and ability proved to be the most effective predictors of screening decision, as evidenced in the final AID tree and the discriminant function.