1. What Matters in College for Retaining Aspiring Scientists and Engineers? Mitchell Chang Jessica Sharkness Christopher Newman Sylvia Hurtado Higher Education Research Institute, UCLA 2010 AERA Annual Meeting Friday, April 30 – Tuesday, May 4 Denver, Colorado

2. Background  U.S. Employment in STEM Demographics National Science Foundation, 2009

3. Background  Persistence in STEM: Higher Education Research Institute, 2010 Percentage of 2004 STEM aspirants who completed STEM degrees within five years

4. Previous Research  Academic preparation  Financial need  Joining a pre-professional or departmental club  Family support, role models, and mentors  Structured research programs

5. Conceptual Model Adapted from Nora, Barlow, Crisp (2005) Pre-College Factors & Pull-Factors Initial Commitments Academic & Social Experiences Persistence Educational Aspirations Formal/Inform al Academics with Faculty Social Experiences Campus Climates Validating Experiences Mentoring Experiences Educational Goal Institutional Commitment Reenrollment in Higher Education Institution Pre-College Ability Psychosocial Factors Financial Assist/Need Encouragement & Support Environmental Pull Factors Final Commitments

6. Research Questions  Among all students who started college with an interest in majoring in a STEM field, are there significant differences in the proportion of URM students (versus Whites and Asians) who follow through on these intentions? If so, are these differences moderated by college experiences?  What factors contribute to the STEM major persistence of URM students?

7. Data  Data Source and Sample: Longitudinal data: 2004 CIRP Freshman Survey & 2008 CIRP Senior Survey  3,670 students overall  1,634 URM students (812 Latino, 626 Black, 196 Native American)  Dependent Variable (“STEM persistence”): Students followed through with first-year intentions to major in STEM field (1), student switched to another major (0) STEM persistence, by race

8. Analyses  Hierarchical Generalized Linear Modeling (HGLM) Appropriate for multi-level data with dichotomous outcome  Two stages of analyses: 1. HGLM analysis of student-level predictors of STEM persistence for all-student sample, focusing on significance of race effects 2. HGLM analysis of student and institution-level predictors of STEM persistence for URM students only  Significant predictors reported as delta-p (Δ-p) statistics

9. Results: Research Question 1 Are there significant differences in the proportion of URM students (versus Whites/Asians) who follow through on STEM major intentions? Model 1Model 2 Race Main Effects Native AmericanNo Latino/aYes (-)No Black/African AmericanYes (-)No Blocks of variables included in the model: Gender, mother’s ed.XX College ExperiencesX  Latinos, Blacks significantly less likely to persist in STEM (vs. Asians & Whites) when only demographics are considered  Effect is moderated by college experiences

10. Results: Research Question 2 What factors contribute to the STEM major persistence of URM students?  Only significant predictors shown Student-Level PredictorsDelta-P High School Academic Preparation/Pre-College Characteristics Math + Verbal SAT score (100-pt increments) 6.8% Academic Self-Concept 1.0% Social Self-Concept -0.8% Aspire to medical degree (vs. Bachelor’s) -11.5% College Experiences Participated in undergrad research program 17.4% Studied with other students 13.6% Joined club/org. related to major 9.3% Faculty interaction (factor) -6.8% Worked full-time while in school -9.7%

11. Results: Research Question 2 What factors uniquely contribute to the STEM major persistence of URM students? Institution-Level PredictorsDelta-P Institutional Characteristics Institutional Selectivity (100-pt increments) -13.0% Percent of students majoring in STEM (10-point increments) 5.57% Model Statistics Explained variance at Level 2 0.69 Baseline probability of STEM major persistence 0.58  Only significant predictors shown

12. Discussion  Experiences in college and college contexts can have a significant influence on student persistence in STEM majors, above and beyond high school preparation Sponsored Research Programs (MARC, MBRS, etc.) Studying with other students Institutional Selectivity Proportion of students majoring in STEM

13. Contact Information Acknowledgments: This study was made possible by the support of the National Institute of General Medical Sciences, NIH Grant Numbers 1 R01 GMO71968-01 and R01 GMO71968-05 as well as the National Science Foundation, NSF Grant Number 0757076. This independent research and the views expressed here do not indicate endorsement by the sponsors. Papers and reports are available for download from project website: http://heri.ucla.edu/nih Project e-mail: herinih@ucla.edu Faculty and Co-PIs: Sylvia Hurtado Mitchell Chang Monica Lin Gina Garcia Felisha Herrera Postdoctoral Scholars: Kevin Eagan Josephine Gasiewski Administrative Staff: Aaron Pearl Graduate Research Assistants: Christopher Newman Minh Tran Jessica Sharkness Cindy Mosqueda Juan Garibay