Logistic Regression Analysis of Graduate Student Retention

Abstract

Logistic regression analysis was utilized to predict the retention of 477 master's and 124 doctoral candidates at a large Canadian university. Selected demographic (e.g., sex, marital status, age, citizenship), academic (e.g., GPA, discipline, type of study, time to degree completion) and financial support variables (e.g., funding received from internal and external scholarships and from research, graduate and teaching assistantships) were used as independent variables. The dichotomous dependent variable was whether the student successful- ly completed the degree. Results for master's students indicate that higher graduate GPAs, increased length of time in the program, increased funding from all sources, full- or part-time registration status in the coursework only program, and full-time registration status in the coursework plus major research paper program significantly improve the student's chances of graduating with the degree. For doctoral candidates, only increased length of time in the program and increased funding from all sources significantly increase the chances of graduating with the doctorate.