Graduation Year

2020

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Adult, Career and Higher Education

Major Professor

Thomas E. Miller, Ed.D.

Committee Member

Karla Davis Salazar, Ph.D.

Committee Member

Michelle Bombaugh, Ph.D.

Committee Member

W. Robert Sullins, Ed.D.

Keywords

community college students, graduation, logistic regression, predictive analysis

Abstract

The purpose of this study was to analyze the ability of demographic and academic pre-matriculation variables to predict degree completion of transfer students at the University of South Florida –Tampa. The University is situated in a state with performance-based funding and with a high number of students who transfer from Florida College System community and state colleges to the university. Transfer students are predicted to become an additional population included in performance metrics thus increasing the need for the university to begin to analyze degree completion barriers to shape early intervention.

Participants in the study were 970 students who transferred with an AA from a Florid College System institution directly to USF-Tampa and enrolled full-time in fall 2014. Logistic regression was used on demographic variables of race, gender, age, and Pell Grant eligibility to determine the relationship to degree completion within three years of transfer enrollment. Logistic regression was also implemented on academic variables of transfer GPA, declared major, and originating community college to determine the predictive relationship to degree completion within three years of transfer enrollment. The study then included an analysis of the significant predictability of the variables and the strength of the predictive model.

Demographic variables had no significant predictive relationship with degree completion. Academic variables of transfer GPA and declared major were found to have significant predictive relationships to degree completion. Transfer GPA was found to have a positive predictive relationship, and academic major was found to have a negative predictive relationship with academic major being more predictive than GPA. The model was found to accurately predict degree completion with a 20% variance.

These results provide information for pre-matriculation advising. Advisors can identify students who are at risk to not complete their degree within three years and can give appropriate guidance in class and major selection. This model also creates a foundation the university can build upon to add variables for increased predictive strength of the model. Predictive models have been instrumental to allow universities to create individual interventions in persistence and degree completion.

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