How Institutional Context Affects Degree Production and Student Aspirations in STEM 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  Push toward accountability standards  Relative homogeneity among researchers in science, technology, engineering, and mathematics (STEM) careers  Research puts onus on students

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?

Theory and Literature: Economic Production Functions

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

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

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

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

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

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

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 ( ) ○ Structural characteristics ○ Aggregated climate elements ○ Production efficiency scores from SFA model

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

Results: Degree Aspirations – Institutional Predictors Master’s Degree M.D.J.D.Ph.D. Control: Private ++++ HBCU ++ Agg. faculty support +++ Agg. cross-racial interactions +++ Production efficiency NS

Results: Degree Aspirations – Individual Predictors Master’s Degree M.D.J.D.Ph.D. Undergraduate research participation ++ Grad school prep. program +++ Faculty support + College GPA ++++ Find a cure to a health problem + Make a theoretical contribution to science + Be well-off financially +-

Limitations  Secondary data analysis  Limited controls for institutional (student and faculty) quality in SFA model  Timeframe of surveys limits causal inferences  Low longitudinal response rate

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

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