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Moving Beyond Frontiers:

How Institutional Context Affects Degree Production and Student Aspirations in STEM. Moving Beyond Frontiers:. Kevin Eagan, Ph.D. University of California, Los Angeles January 28, 2010. The Problem. Higher institutional graduation rates in non-STEM fields relative to STEM fields

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Moving Beyond Frontiers:

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  1. How Institutional Context Affects Degree Production and Student Aspirations in STEM Moving Beyond Frontiers: Kevin Eagan, Ph.D. University of California, Los Angeles January 28, 2010

  2. The Problem • Higher institutional graduation rates in non-STEM fields relative to STEM fields • Push toward accountability standards • Relative homogeneity among researchers in science, technology, engineering, and mathematics (STEM) careers • Research puts onus on students

  3. Research Questions Institutions’ STEM Degree Production • What institutional characteristics affect the production of undergraduate STEM degrees? • What factors contribute to institutions’ efficiency at producing undergraduate STEM degrees? Students’ Degree Aspirations • What student characteristics predict student degree aspirations at the end of four years of college? • What institutional characteristics predict student degree aspirations at the end of four years of college? • Do these student and institutional variables have differential effects across specific groups of students?

  4. Theory and Literature: Economic Production Functions

  5. Theory and Literature: Degree Aspirations • Status attainment theory (Blau & Duncan, 1967; Sewell, Haller, & Portes, 1969) • College student socialization (Weidman, 1989) • Primary limitations of degree aspiration studies: operationalization of the dependent variable, under-development of institutional problem, and analytic methods

  6. Methods: Production Function • Data: Integrated Postsecondary Educational Data System (IPEDS) • Sample: 4-year public and private non-profit bachelor’s degree granting institutions (N=1,428) across 4 years • Subsample for additional analyses: 197 public and private, non-profit four-year institutions

  7. Methods: Production Function • Dependent Variables • DV1: total undergraduate STEM degrees produced each year • DV2 (created from first analysis): production efficiency score for each institution-year case • Independent variables: • Production function: human capital, labor, financial capital • Efficiency analysis: selectivity, structural characteristics, climate elements

  8. Methods: Production Function • Analyses • Stochastic frontier analysis • Decomposes error term into two components: randomly distributed error and non-randomly distributed error (inefficiency) • More robust than OLS regression • Distinct from data envelopment analysis, as SFA accounts for external shocks to the firm • Hierarchical Linear Modeling • Analyze the relative contributors to production efficiency

  9. Production Function Results • Decreasing returns to scale • Average efficiency score: 40% • Efficiency • Negatively affected by: % PT faculty, % URM students • Positively affected by: % PT students, % STEM students, selectivity

  10. Methods: Degree Aspirations • Data • Students • 2004 Freshman Survey • 2008 College Senior Survey • National Student Clearinghouse • Institutions • IPEDS • Student-level aggregates • SFA model (efficiency score) • Sample: 5,876 students across 197 institutions

  11. Methods: Degree Aspirations • Dependent variable: recoded degree aspirations into five categories • Independent variables • Background characteristics (2004) • Pre-College characteristics (2004) • Connections to peers and faculty (2008) • Campus involvement (2008) • Campus climate perceptions (2008) • Institutional characteristics (2004-2008) • Structural characteristics • Aggregated climate elements • Production efficiency scores from SFA model

  12. Methods: Degree Aspirations • Analyses • Response weights • Multinomial hierarchical generalized linear modeling • Categorical, non-ranked outcome • Nested data (students within institutions) • Model building

  13. Results: Degree Aspirations – Institutional Predictors

  14. Results: Degree Aspirations – Individual Predictors

  15. Limitations • Secondary data analysis • Limited controls for institutional (student and faculty) quality in SFA model • Timeframe of 2004-2008 surveys limits causal inferences • Low longitudinal response rate

  16. Discussion • Limitation of applying economic theory and efficiency to higher education • Balancing democratic mission of higher education with political and economic realities • Student preparation • Faculty employment • Program duplication and coordination • Engagement with diversity

  17. Implications for Research • Institutional data • Utility of efficiency scores in higher education • Self-selection bias and causality

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