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
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

Research Questions
1.
2.
3.
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?
If URMs have significantly lower GPAs, can
these differences be explained by studentlevel characteristics?
If not, can institutional characteristics
account for the differences?
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
Literature Review: Institutional
Factors Predicting GPA
◦
◦
◦
◦
◦
Type
Selectivity
Size
Racial Isolation
HBCU status
Limitations of the literature

Research does not:
◦
◦
◦
◦
Use diverse sample of students
Disaggregate by race
Use longitudinal data
Collect multi-institutional data
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
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+
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
Institution-level Variables

Institution-level:
◦
◦
◦
◦
◦
◦
Type
Control
Percent of students majoring in STEM
Structural diversity
Selectivity
HBCU status
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
Limitations
Generalizability
 Response rate
 Limitations of GPA

Validity of self-reported GPA

Comparison of registrar-provided GPA data, selfreported GPA
◦ Sub-sample: N = 2,568
Correlation between self-reported GPA, actual
GPA = 0.77
 Recoded CIRP GPA scale to match registrar 4point 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
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
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 (+)
HLM Results:
Student major

Academic Major in 2008 (compared to
non-STEM)
◦
◦
◦
◦
Health sciences (+)
Physical sciences (-)
Engineering (NS)
Biology (NS)
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
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
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

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.)
Contact Information
Faculty and Co-PIs:
Sylvia Hurtado
Mitchell Chang
Postdoctoral Scholars:
Kevin Eagan
Josephine Gasiewski
Administrative Staff:
Aaron Pearl
Monica Lin
Graduate Research Assistants:
Tanya Figueroa
Cindy Mosqueda
Christopher Newman
Gina Garcia
Juan Garibay
Minh Tran
Felisha Herrera
Jessica Sharkness
Papers and reports are available for download
from project website:
http://heri.ucla.edu/nih
Project e-mail: herinih@ucla.edu
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.