Moving Beyond Frontiers:
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Transcript of Moving Beyond Frontiers:
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How Institutional Context Affects Degree Production and Student
Aspirations in STEM
Kevin Eagan, Ph.D.University of California, Los Angeles
January 28, 2010
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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
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Research QuestionsInstitutions’ 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?
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Theory and Literature: Economic Production Functions
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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
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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
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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 capitalEfficiency analysis: selectivity, structural
characteristics, climate elements
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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 firmHierarchical Linear Modeling
○ Analyze the relative contributors to production efficiency
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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
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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
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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
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Methods: Degree Aspirations Analyses
Response weightsMultinomial hierarchical generalized linear
modeling○ Categorical, non-ranked outcome○ Nested data (students within institutions)○ Model building
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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 NS NS NS
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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 + -
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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
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Discussion Limitation of applying economic theory
and efficiency to higher education Balancing democratic mission of higher
education with political and economic realitiesStudent preparationFaculty employmentProgram duplication and coordinationEngagement with diversity
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Implications for Research Institutional data Utility of efficiency scores in higher
education Self-selection bias and causality