Assessing Differential Prediction of College Grades by Race/Ethnicity with a Multilevel Model

Previous research notes the importance of understanding racial/ethnic differential prediction of college grades across multiple institutions. Institutional variation in selection indices is especially important given some states' laws governing public institutions' admissions decisions. This paper employed multilevel moderated multiple regression to study the variation of selection indices across 30 institutions and the accuracy of selection indices in predicting college grades for students of different racial/ethnic backgrounds. Several benefits of multilevel models for cross-institutional differential prediction studies were described and include: controlling for institutional differences in range restriction, providing reliability estimates of least squares estimates, and adjusting criterion scores for differences in coursework difficulty. The findings from this study provide evidence of institutional variation in selection indices, which challenges current laws aimed at standardizing them. Specifically, there was evidence that the predictor slope coefficients varied across institutions, in addition to the estimates that measured intercept differences for African and Asian American students. Across universities, the results mirrored previous findings: high school grade point average (GPA) differentially predicted grades for African Americans, SAT verbal scores differentially predict grades for Asian Americans, and SAT math scores were better predictors of Asian Americans' grades.