Beyond the GRE: Using a Composite Score to Predict 
the Success of Puerto Rican Students in a Biomedical 
PhD Program

The use and validity of the Graduate Record Examination General Test (GRE) to predict the success of graduate school applicants is heavily debated, especially for its possible impact on the selection of underrepresented minorities into science, technology, engineering, and math fields. To better identify candidates who would succeed in our program with less reliance on the GRE and grade point average (GPA), we developed and tested a composite score (CS) that incorporates additional measurable predictors of success to evaluate incoming applicants. Uniform numerical values were assigned to GPA, GRE, research experience, advanced course work or degrees, presentations, and publications. We compared the CS of our students with their achievement of program goals and graduate school outcomes. The average CS was significantly higher in those students completing the graduate program versus dropouts (p < 0.002) and correlated with success in competing for fellowships and a shorter time to thesis defense. In contrast, these outcomes were not predicted by GPA, science GPA, or GRE. Recent implementation of an impromptu writing assessment during the interview suggests the CS can be improved further. We conclude that the CS provides a broader quantitative measure that better predicts success of students in our program and allows improved evaluation and selection of the most promising candidates.