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Selection Bias in Educational Debt Decisions: Analyzing the Impacts on Enrollment in Master’s Degree Programs

Selection Bias in Educational Debt Decisions: Analyzing the Impacts on Enrollment in Master’s Degree Programs. Alee Lynch-Gunderson, PhD Student Dr. Pete Villarreal III, Faculty University of Florida School of Human Development and Organizational Studies Higher Education Administration.

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Selection Bias in Educational Debt Decisions: Analyzing the Impacts on Enrollment in Master’s Degree Programs

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  1. Selection Bias in Educational Debt Decisions: Analyzing the Impacts on Enrollment in Master’s Degree Programs Alee Lynch-Gunderson, PhD Student Dr. Pete Villarreal III, Faculty University of Florida School of Human Development and Organizational Studies Higher Education Administration

  2. Selected Citations • Millett (2003) “How Undergraduate Loan Debt Affects Application and Enrollment in Graduate or First Professional School” • Purpose: Effects of debts on students who are most likely prospects for entering graduate and professional school • Methodology: Logistic regressions • Sample: Recent bachelor’s degree recipients who expect to earn a doctoral degree (1,982 cases) • Dataset: Baccalaureate and Beyond: 93/97 • Key Limitation: Dataset did not distinguish types of financial support

  3. Selected Citations • Malcolm & Dowd (forthcoming) “College Student Debt as Opportunity or Disadvantage? A Reconceptualization and Application to STEM Graduate Enrollment” • Purpose: Effect of debt on graduate school attendance of STEM majors • Methodology: Propensity Score Matching • Sample: STEM Bachelor’s recipients from 2000-01 & 2001-02 (7,700 cases) • Dataset: 2003 National Survey of Recent College Graduates, 2002-2003 College Board Annual Survey of Colleges and Universities, Institute for College Access and Success, Integrated Postsecondary Education Data System, & Barron’s Profiles of American Colleges • Key Limitation: Exclusion of other master’s degree programs

  4. Selected Citations • Perna (2004) “Understanding the Decision to Enroll in Graduate School: Sex and Racial/Ethnic Group Differences” • Purposes: • Contribute to understanding of underrepresentation of women, African Americans, and Hispanics among doctoral and professional degree enrollees • Test a conceptual model for graduate school enrollment • Method: Multinomial logit models • Sample: Bachelor’s degree recipients in 1992-93 (9,241 cases) • Dataset: Baccalaureate and Beyond: 93/97 • Key Limitation: Did not control for self-selection bias

  5. Cultural & Social Capital • Parental educational attainment • Primary language at home is English • Values additional education (B&B 11-item) • Parental monetary contribution • Carnegie classification • Tuition • Location • Attended two-year college

  6. Expected Costs & Benefits • Net price • Foregone earnings by undergraduate major • Time horizon (delayed college) • Marital Status • Parental Status

  7. Financial & Academic Resources • Undergraduate Educational Debt • Dependency status • Time to Bachelor’s Degree • Cumulative Undergraduate GPA • SAT/ACT quartile

  8. Financial Resources • Undergraduate Educational Debt • Dependency status • Type of Assistantship

  9. Academic Resources • Time to Bachelor’s Degree • Cumulative Undergraduate GPA • SAT/ACT quartile

  10. Cultural & Social Capital • Parental educational attainment • Primary language at home is English • Values additional education (B&B 11-item) • Parental monetary contribution • Carnegie classification • Tuition • Location • Attended two-year college • Parent’s have a mortgage • Location of employment

  11. Research Study • How does the likelihood of master’s program enrollment vary by level of undergraduate educational debt? • Contributions to Current Body of Research • Utilize propensity score methods to control for self-selection bias • Control for differing effects between types of financial support • National dataset includes students from all master’s degree program areas • Dataset: National Postsecondary Student Aid Study 2008

  12. What’s Next? • January – Submission for an AIR Research Grant • January to May – Conduct analyses • May – Submission for research presentation at ASHE • December – Submit to journal for publication

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