Grades in Doctoral Study in Business and Management: An Empirical Bayes Approach

A validity study was conducted to examine the degree to which GMA T scores and undergraduate grade-point average (UGPA) could pre-dict first-year average (FYA) and final grade-point average in doc-toral programs in business. A variety of empirical Bayes regression models, some of which took into account possible differences in regressions across schools and cohorts, were investigated for this purpose. Indexes of model fit showed that the most parsimonious model, which did not allow for school or cohort effects, was just as useful for prediction as the more complex models. The three pre-admissions measures were found to be associated with graduate school grades, though to a lesser degree than in MBA programs. The prediction achieved using UGPA alone as a predictor tended to be more accurate than that obtained using GMAT verbal (GMATV) and GMAT quantitative (GMATQ) scores together. Including all three predictors was more effective than using only UGPA. The most likely explanation for the lower levels of prediction than in MBA programs is that doctoral programs tend to be more selective. Within-school means on GMATV, GMATQ, UGPA, and FYA were higher than those found in MBA validity studies; within-school standard deviations on FYA tended to be smaller. Among these very select, academically competent doctoral students, highly accurate predic-tion of grades may not be possible. The author is grateful to the many people who assisted in conducting this project. Anne Ninneman managed the data collection with the assistance of Georgiana Thurston; Doug Gentile developed systems for organizing the data base and, alon