Dissertation Title: Socioeconomic Status and Decisions about Postsecondary Education

Committee: Stephen Raudenbush (Co-Chair), Geoffrey Wodtke (Co-Chair), Ariel Kalil (UChicago, Harris School of Public Policy)

My research focuses on education, inequality, the family, and policy. In my dissertation, I investigate inequality in access to postsecondary education between 1970 and 2012, using multiple nationally representative survey datasets. I explore how students and parents form conclusions about a student’s future options. I show that students from low SES backgrounds are less likely to believe they are qualified for four-year college attendance than their more advantaged peers with similar grades and test scores. This leads low SES students to opt out of applying to colleges for which they are qualified. Students and parents from less advantaged backgrounds are also less likely to perceive four-year college attendance as an option financially. However, they overestimate tuition costs. My research advances policy understanding of inequality in college attendance by showing that inaccurate information leads many low SES students to opt out during the college application stage. I am also involved in work using causal inference and machine learning methods to assess how over 200 measures of elementary school context mediate neighborhood effects on academic achievement. Additionally, I have experience advising on multiple mixed-methods projects, bringing in interview and survey data to supplement quantitative findings and better understand program and policy effectiveness.

Background:

In my research, I investigate inequality in access to and completion of higher education in the United States. I am interested in the access that high school students and their parents have to information and resources regarding college attendance and costs during the college planning process. I am additionally interested in the importance of neighborhood and school effects on students' educational outcomes. I primarily use quantitative research methods, including survey data analysis, multilevel modeling, and causal inference techniques.