moving beyond frontiers:

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 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? 

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 capitalEfficiency 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 firmHierarchical 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 (2004-2008)

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

Methods: Degree Aspirations Analyses

Response weightsMultinomial 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 NS NS 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 2004-2008 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 realitiesStudent preparationFaculty employmentProgram duplication and coordinationEngagement with diversity

Implications for Research Institutional data Utility of efficiency scores in higher

education Self-selection bias and causality