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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Education (also CAGS)

Year Degree Awarded

2018

Month Degree Awarded

May

First Advisor

Ryan S. Wells

Subject Categories

Disability and Equity in Education | Higher Education | Science and Mathematics Education

Abstract

This dissertation examined the science, technology, engineering, and math (STEM) major declaration intentions of students with disabilities as they graduated high school and entered college. I used data from the High School Longitudinal Study of 2009 (HSLS:09) because data collection began in high school and followed students into college, facilitating research focusing on access. Before investigating major declaration intentions, I critiqued the definition and measurement of disability in the HSLS:09, drawing from survey research methods literature. The two subsequent analyses focused on psychological and structural components, respectively. My focus on psychological components drew from Eccles and colleagues’ (1983) expectancy-value framework. This framework tapped into the valuation that students placed on math- and science-related concepts and their expectations to succeed in those fields. Structural components explored in the final analysis drew from human, cultural, and social capital theories. These three theories were at the core of Perna’s (2006) model of college choice, which I adapted to predict majoring in STEM. Both analyses utilized multiple logistic regression to create prediction models. Findings suggested that college-bound students with ADHD have higher odds of intending to pursue STEM majors, compared to students experiencing other forms of disability. Psychological and structural measures were also positively related with odds of pursuing these majors. Implications highlight avenues for enhancing STEM participation for students with disabilities, offer suggestions for improvements to future data collection efforts, and lend guidance for future researchers looking to study disability using the HSLS:09 or other secondary data.

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