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We considered the problem of predicting student retention using logistic regression when the most important covariates such as the college variables are latent, but the network structure is known. This network structure specifies the relationship between pre-college to college variables and then from college to student retention variables. Based on this structure, we developed three estimators, examined their large-sample properties, and evaluated their empirical efficiencies using WKU student retention database. Results show that while the hat estimator is expected to be most efficient, the tilde estimator was shown to be more efficient than the check method. This increased efficiency suggests that utilizing the network information can improve our predictions.

Long-term declines in birth rates pose a threat to the economic viability of higher education institutions (HEIs), and these institutions must strategically plan for these changes. Increasing the enrollment and retention of underrepresented Black and Latinx students is one potential strategy to offset declining numbers of high-school graduates. While educational attainment has increased overall during the last two decades, Black and Latinx students continue to have lower educational attainment levels than White students. This study uses quantitative data from the Integrated Postsecondary Education Data System (IPEDS) from 2000 to 2018 to estimate enrollment and retention levels of Black, Latinx, and White students in Higher Educational Institutions (HEIs) in the New England Region (NER). The study estimates correlations between Black, Latinx, and White enrollment and retention levels, revealing a negative impact on Black and Latinx enrollment and retention as White enrollment increases. This research likewise reveals a decrease in White enrollment as Black and Latinx enrollment increases. Additionally, this study uses a K-means cluster analysis to understand the association between enrollment and retention level performance of NER HEIs. Findings from two different cluster analyses show 1) a negative pattern of retention of Blacks and Latinx students as enrollment for these populations increases in HEIs and 2) a positive retention pattern for a subset of HEIs with higher retention levels with lower numbers of Black and Latinx enrollment totals. This proposal describes and interprets these findings and proposes new research examining institutional characteristics that may give rise to Blacks and Latinx students' retention levels. This study considers a wide range of institutional characteristics, including supportive programming and the types of financial aid packages specifically designed to retain students, while accounting for endowment and institutional size. This dissertation aims to provide NER HEI administrators with data that can inform short and long-term strategic planning. / Business Administration/Interdisciplinary

This research study explored the predictive value of psychological grit in determining student retention among postgraduates in an Open Distance Learning (ODL) institution in South Africa. Working from within a quantitative framework (and adopting a correlational research design), an online version of the Grit-S scale was utilised to gauge participants’ levels of grit (n = 837), followed by one-year lagged secondary data which sought to ascertain retention among the sample. Seeking to explore the psychometric rigour of the Grit-S scale, exploratory and partial confirmatory factor analyses were employed to investigate the validity of the instrument, followed by the assessment of Cronbach’s alpha coefficients. Thereafter, correlations and binary logistic regressions were employed to investigate the relationship between the constructs and explore grit’s ability to predict retention from one academic period to the next. Results from the analyses indicated that, while the Grit-S scale demonstrates sound validity and reliability for use within ODL settings, grit was neither related to retention nor could it significantly predict retention among the current sample. Although these results do not negate grit’s reported role in determining successful student outcomes in traditional higher education settings, they bring to the fore a need to critically re-examine grit’s contribution to understanding retention, not only among ODL students, but among those whose socio-economic circumstances remain a crucial barrier. As such, pre- emptive strategies aimed at retaining students should rather focus on mitigating the immediate, and often negative, socio-economic circumstances faced by students, rather than intervening based on their levels of psychological grit. / Psychology / D. Litt. et Phil. (Psychology)

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First-year experience programs on college and university campuses are designed to support the academic, social, and institutional transition needs of the first-year student. Retention on campuses has been an issue that educational leaders have been addressing for many years. On average, approximately 20% to 30% of first-year students will not return for the second year. A review of the literature shows that there is a positive impact of such programs on a student’s achievement and retention on college campuses. It also reflects the various factors outside of an institution’s control that can have an impact on the student’s achievement. This qualitative study included one case study that analyzed the experiences of thirteen (13) first-year students who participated in a first-year experience program consisting of an extended orientation camp and an intentional programming model on large, public, 4-year, urban campus in the Southern United States. Findings and discussion from the study revealed: (a) the perceptions of impact that first-year programs have on a student’s belonging, adjustment, success, and support after the first semester of college; and (b) common themes and trends throughout the first six weeks of the participants related to their perceptions of belonging, adjustment, success, and support. Implications of this study also prescribe future research that could explore specific resources, learning outcomes, and retention rates across other urban institutions.

Student Success continues to be a topic of great interest in the Higher Education Literature. Fifty percent of those students who enter a four-year institution actually graduate and 25 % of first year students do not persist into their second year in college. First-year success courses and peer mentoring along with other programming strategies have been developed to improve retention and success for college students during their first-year of college. This study explored how college students from nine different institutions defined college student success. In addition, students from these institutions were surveyed to find out how and if first-year success courses and/or peer mentoring contribute to college student success. Follow-up interviews allowed for a deeper understanding of how first-year success courses and peer mentoring contribute to college student success. The study found a new comprehensive definition for college student success. Also, first-year success courses and peer mentoring do have positive relationships with college student success however, they also have some shortcomings that were identified in this study.

Event history analysis is the most prevalent modeling technique used to model event occurrence with longitudinal data (Cox & Oakes, 1984; Menard, 1991; Singer & Willett, 1993, 2003). An alternative is to model longitudinal data within the SEM framework, known as latent variable growth modeling (McArdle, 1988; Meredith & Tisak, 1990), which can provide a more robust framework. Whether or not a student remains in college presents an appropriate context within which to examine the modeling of event occurrence with longitudinal data. The purpose of the study was to compare event history and latent growth modeling as for predicting change in college student perseverance, with college student persistence literature serving as the framework. Students are defined as having persevered if they have earned hours and the end of the semester rather than if they are enrolled at the beginning of the semester, which is the traditional definition of persistence.The population for the study was the 2001 and 2002 cohorts of first-time, full-time freshmen at a large mid-Atlantic urban research university. Stopouts and transfer students were excluded. Data was analyzed for the first five semesters for each cohort. The results showed that parameter estimates were quite consistent across model type and time frame and were mostly consistent with previous research. No one method outperformed the others in terms of predicting correct classification. Using event history analysis with the structural equation modeling framework, however, appeared to be a very promising alternative to event history analysis with logistic regression since one can model error term and examine the differential effects of predictors at each time period. Finally, while latent growth modeling did not outperform the other methods in predictive classification, the study demonstrated it can be used for event occurrence analysis to test more complex theories.