1. Academic Achievement among STEM Aspirants: Why do Black and Latino Students Earn Lower Grades than their White and Asian Counterparts? Jessica Sharkness, M. Kevin Eagan Jr., Sylvia Hurtado, Tanya Figueroa, Mitchell J. Chang University of California, Los Angeles

2. Introduction Racial disparities in undergraduate STEM completion rates and graduate enrollment rates Lower GPAs associated with disinterest in STEM and higher attrition rates GPA: Important, but limited

3. Research Questions 1. Among students who entered college with an interest in majoring in a STEM field, do URM students graduate with lower cumulative college GPAs than White students, after controlling for relevant background characteristics? 2. If URMs have significantly lower GPAs, can these differences be explained by student- level characteristics? 3. If not, can institutional characteristics account for the differences?

4. Literature Review: Student-Level Factors Predicting GPA ◦ All Students ◦ Self-perceptions ◦ Approaches to studying ◦ Social activity ◦ Relationships with peers/faculty ◦ STEM Students ◦ Climate ◦ Supplemental instruction and academic support ◦ Undergraduate research ◦ URM Students ◦ Faculty mentors ◦ Social connections ◦ Racial isolation

5. Literature Review: Institutional Factors Predicting GPA ◦ Type ◦ Selectivity ◦ Size ◦ Racial Isolation ◦ HBCU status

6. Limitations of the literature Research does not: ◦ Use diverse sample of students ◦ Disaggregate by race ◦ Use longitudinal data ◦ Collect multi-institutional data

7. Sample UCLA’s Cooperative Institutional Research Program (CIRP) ◦ The Freshman Survey (TFS) 2004 ◦ College Senior Survey (CSS) 2008 ◦ Intentional sampling of students  Sample is 24% Black, 23% Latino, 12% Asian American, & 34% white  55% Female ◦ 4,122 students in 224 Institutions

8. Dependent Variable Dependent variable ◦ Self-reported measure of students’ cumulative GPA as of the time that the CSS was given. ◦ Scale of 1 to 8, 1 = D to 8 = A or A+

9. Student-level Independent Variables Student-level ◦ Demographics (race, gender) ◦ Pre-college achievement (HS grades, composite SAT score) ◦ Self-rated abilities and high school activities ◦ Final academic major (non-STEM reference group) ◦ College experiences  Faculty interaction and mentorship  Extracurricular activities  Academic engagement  Perceptions of the campus climate

10. Institution-level Variables Institution-level: ◦ Type ◦ Control ◦ Percent of students majoring in STEM ◦ Structural diversity ◦ Selectivity ◦ HBCU status

11. Analysis Missing data Weighting Validity of self-reported GPA Hierarchical linear modeling Six models Control for race in model 1; follow the effects of race across next five models

12. Limitations Generalizability Response rate Limitations of GPA

13. Validity of self-reported GPA Comparison of registrar-provided GPA data, self- reported GPA ◦ Sub-sample: N = 2,568 Correlation between self-reported GPA, actual GPA = 0.77 Recoded CIRP GPA scale to match registrar 4- point scale 90% of students accurately reported their GPA within 0.3 points, equal numbers higher and lower ◦ Over-reporting highest among lowest GPA students (under 2.7), but less than 10% of sample

14. Descriptive Results Students ◦ Average GPA between a B and B+ ◦ Average SAT score = 1154 ◦ 40% of students switch to non-STEM field major by senior year Institutions ◦ 44% publicly controlled ◦ 36% research universities ◦ 10% HBCU ◦ Average selectivity: 1107

15. HLM Results: Pre-college factors Background Factors ◦ Gender: Female (+) ◦ Race: Black (-) ◦ Race: Latino (-) ◦ High School Performance (+) Pre-College Achievement ◦ HS GPA (+) ◦ Composite SAT scores (+) Pre-College Experiences and Self-Rated Abilities ◦ Time management skills (+) ◦ HPW studying (+)

16. HLM Results: Student major Academic Major in 2008 (compared to non-STEM) ◦ Health sciences (+) ◦ Physical sciences (-) ◦ Engineering (NS) ◦ Biology (NS)

17. HLM Results: College Experiences Positive Predictors ◦ Academic Self-Concept ◦ Tutoring other students ◦ Undergraduate research ◦ Taking honors /advanced classes ◦ Graduate school preparation programs ◦ Faculty mentorship Negative Predictors ◦ Worked full-time ◦ Feeling overwhelmed ◦ Sensing competition for grades ◦ Receiving help with study skills from faculty ◦ Discussing coursework with faculty outside of class ◦ Hours per week on social networking sites

18. HLM Results: Institutional context Only one institution-level predictor ◦ Institutional Selectivity (-) Allowed slope for Black, Latino students to vary across institutions ◦ Black – significant ◦ Unable to account for variation with any available variables

19. Discussion Participation in certain academic activities Socio-emotional challenges Faculty mentorship Racial differences in grades persist Differences in GPA across majors Black student GPA varies by institutional context

20. Conclusions and Implications Unclear what accounts for the effect on race ◦ Differences across classroom contexts? Future research: ◦ Investigate opportunity structures and inequalities in STEM classrooms ◦ Add more contextual (institution-level) measures (grading on a curve, outreach programs, campus climate, etc.)

21. 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 Tanya Figueroa Gina Garcia Felisha Herrera Postdoctoral Scholars: Kevin Eagan Josephine Gasiewski Administrative Staff: Aaron Pearl Monica Lin Graduate Research Assistants: Christopher Newman Minh Tran Jessica Sharkness Cindy Mosqueda Juan Garibay