Date of Award

Spring 5-15-2026

Degree Type

Dissertation

Degree Name

PhD Higher Education Leadership, Management, Policy

Department

Education Leadership, Management and Policy

Advisor

Rong Chen, Ph.D.

Advisor

Rong Chen, Ph.D.

Committee Member

Jennifer Timmer, Ph.D.

Committee Member

Leo Pedraza, Ph.D.

Keywords

college match; STEM student retention; institutional selectivity; academic preparation; educational equity; first-year college outcomes.

Abstract

This quantitative study examines how student–college match relates to first-year STEM retention in a nationally representative cohort of four-year, STEM-intending students from the High School Longitudinal Study of 2009 (HSLS:09) linked with institutional data from the Integrated Postsecondary Education Data System (IPEDS). Building on probabilistic approaches to college match, the study first estimates each student’s predicted probability of admission to institutions in five selectivity tiers using high school GPA, standardized test scores, highest mathematics course completed, AP mathematics credits, gender, and socioeconomic status. It then compares the predicted tier with the selectivity of the institution actually attended to classify students as matched, undermatched, or overmatched. Binary logistic regression models are used to estimate the association between match status and first-year STEM retention defined as continued enrollment in a four-year institution and in a STEM or National Science Foundation supported science major at the second follow-up while controlling for academic preparation, demographic characteristics, family background, and institutional features such as control, size, and geographic region.

Descriptive analyses indicate that overmatch is the dominant pattern among STEM-intending 4-year students, with roughly two thirds enrolling at institutions more selective than their predicted access and that first-year STEM retention is nearly evenly split between students who remain in STEM and those who switch majors or leave their institutions. In multivariate models, neither undermatch nor overmatch emerges as a statistically significant independent predictor of first-year STEM retention, Instead, the strongest and most consistent predictors are students’ highest level of high school mathematics and related indicators of academic preparation.

These findings suggest that student–college match, as operationalized through a probabilistic selectivity framework, operates as a secondary structural correlate of early STEM persistence rather than a dominant or universal predictor. By contrast, academic readiness and institutional opportunity structures (such as institutional control, size, and geographic region) exert a more substantial influence on first-year STEM retention. At the same time, the concentration of overmatch among lower-SES and racially minoritized students, coupled with the lower retention probabilities observed for overmatched Hispanic/Latino students in at least one interaction specification, underscores the need to examine match through an equity lens so that higher education leaders and policymakers can design admissions, advising, and first-year STEM supports that are responsive to both academic alignment and structural inequality. From a practice and policy perspective, the results point to the importance of strengthening pre-college mathematics preparation, refining admissions and enrollment practices that shape match patterns, and developing targeted, data-informed interventions for overmatched students in selective STEM environments.

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