Degree Name

EdD (Doctor of Education)

Program

Educational Leadership

Date of Award

12-2019

Committee Chair or Co-Chairs

Don W. Good

Committee Members

Jill A. Channing, James H. Lampley, Stephanie R. Tweed

Abstract

An ex-pos-facto non-experimental quantitative study was conducted to examine the academic, financial, and student background factors that influence first-to-second year retention of engineering and engineering technology students at U.S. community colleges. Analysis of the five research questions was done using a chi-square test and multiple logistic regressions. Data were obtained from the National Center for Education Statistics (NCES) Beginning Postsecondary Students 2012/2014 (BPS: 12/14) study. Computations were performed using PowerStats, a web-based statistical tool provided by the NCES, as well as IBM SPSS 25.

The sample population consisted of students who entered postsecondary education for the first time in the 2011-2012 academic year and enrolled in an engineering or engineering technology program at a community college. Predictor variables were identified from the dataset and grouped into the categories of academic, financial, and student background variables. These groupings were used as individual models to predict first-to-second year retention of community college engineering and engineering technology students using logistic regressions. Finally, individual variables that displayed statistical significance were then combined and were used as a model to predict student retention with a logistic regression.

Results indicate that community college engineering and engineering technology students are not retained at a significantly different rate than non-engineering and engineering technology majors. In addition, the groupings of academic and student background variables did not have a significant impact on the retention of community college engineering and engineering technology students, while the grouping of financial variables did have a significant impact on retention. The variables attendance pattern (academic), TRIO program eligibility criteria and total aid amount (financial), and dependency status (student background) were all statistically significant to their respective predictor models. Finally, the combination of these statistically significant academic, financial, and student background variables were significant predictors of retention.

Document Type

Dissertation - Open Access

Copyright

Copyright by the authors.

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