Using binary logistic regression to analyze the predictive validity of undergraduate GPA and standardized test scores in forecasting master’s student degree completion
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Stefanelli, James.
Using binary logistic regression to analyze the predictive validity of undergraduate GPA and standardized test scores in forecasting master’s student degree completion. Retrieved from
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TitleUsing binary logistic regression to analyze the predictive validity of undergraduate GPA and standardized test scores in forecasting master’s student degree completion
Date Created2021
Other Date2021-05 (degree)
Extent1 online resource (xi, 141 pages)
DescriptionWedged between undergraduate and doctoral students exists the oft-understudied population of master’s students. The dearth in master’s-specific research is astounding considering master’s degrees are the fastest growing degree credential. Why is there such a lack of information about master’s students from a national perspective in comparison to other degree-seeking students? Perhaps it is because assessment, like Integrated Postsecondary Education Data System (IPEDS), and prestige, like U.S. News & World Report rankings, are not tied to it. In simple terms, a carrot does not exist to incentivize it nor a stick to mandate it. The absence of research has far-reaching implications and manifests primarily as a disconnect between the efficacy of admissions and anticipated student degree completion, but has further implications on our grasp of master's student graduation rates and time to degree. This study bridges this gap by employing binary logistic regression to analyze the relationship between the independent variables; admissions-, demographic-, and environment-based attributes, and the dependent variable master’s degree completion. In looking specifically at the traditional measures of success within the master’s admissions process; undergraduate GPA and standardized test scores, statistical significance was not found. However, the disproportionately high rates of graduation for master’s students is in conflict with these findings. Within the environment attributes; institution, academic discipline, attendance pattern, start term, representation by race/ethnicity, and representation by gender, the model indicated statistical significance across categories within institution, academic discipline, attendance pattern, and start term. Certain institutions and academic disciplines are associated with greater odds of graduation than others, full-time students more likely to graduate than their part-time peers, as are students that start their program in the Fall term when compared to those that start in the Spring. Finally, within the demographic attributes; age, race/ethnicity, gender, citizenship, and residency, only age was statistically significant. Simply, the odds of graduation decrease as students grow older. Additionally, this study provides university administrators with a comparative benchmark for both master's student graduation rates and time to degree. This is the first-ever, multi-university study to employ a statistical model to analyze the predictive validity of the traditional measures of success, and accompanying environmental and demographic attributes, in forecasting degree completion. It is also the most comprehensive in terms of program breadth.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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