2014 - What Matters in College for Retaining Aspiring Scientists and Engineers from Underrepresented Racial Groups

Attribution: Chang, Mitchell, Sharkness, Jessica, Hurtado, Sylvia, & Newman, Christopher B.
Researchers: Christopher B. NewmanJessica SharknessMitchell ChangSylvia Hurtado
University Affiliation: University of California Los Angeles; Tufts University; University of San Diego
Email: jchang@gseis@ucla.edu
Research Question:
Among students who started college with an interest in majoring in a STEM field, does a student's race contribute significantly to the chances that he or she will follow through on these intentions? If so, are the effects of race moderated by high school academic preparation and/or key college experiences? If there are racial disparities in persistence rates after controlling for pre-college student characteristics, what are the college factors that contribute to the persistence of under represented racial minority (URM) students? What college experiences and institutional characteristics significantly predict the likelihood that a URM student will follow through on his or her intentions to pursue a degree in STEM?
Published: Yes
Journal Name or Institutional Affiliation: Journal of Research in Science Teaching
Journal Entry: Vol. 51, No. 5, Pp. 555-580
Year: 2014
Findings:
  1. African American and Latino students were less likely to persist in STEM majors in comparison to White and Asian students.
  2. The effect of racial classification on 4-year STEM persistence, controlling for high school preparation, experience, and demographic characteristics, was not statistically significant.
  3. After taking college experiences into account, the race variables did not exhibit a significant effect on STEM persistence.
  4. Among the high school academic preparation predictors, only SAT scores were significantly associated with STEM persistence for URM students.
  5. Percent of study body that are URM had no significant effect on URM persistence in STEM in college.
  6. Percent of students receiving federal aid had no significant effect on URM persistence in STEM in college.
  7. Findings from the follow-up analysis of the sample of URMs suggest that institutions can improve URM STEM persistence by increasing the likelihood that those students will engage in key academic experiences: studying frequently with others, participating in undergraduate research, and involvement in academic clubs or organizations.
  8. Pre-college factors may explain some of the observed racial disparities and that individual institutions can take more concrete actions to improve science achievement.
  9. Pre-college factors are important in explaining racial disparities in science achievement.
Scholarship Types: Journal Article Reporting Empirical ResearchKeywords: Achievement GapMinoritiesPersistenceRaceRetentionSTEMStudent ExperienceRegions: NationalMethodologies: QuantitativeResearch Designs: Secondary Survey DataAnalysis Methods: Descriptive StatisticsHGLM (Hierarchical Generalized Linear Modeling) Sampling Frame:College Students
Sampling Types: Nationally RepresentativeAnalysis Units: SchoolStudentData Types: Quantitative-Longitudinal
Data Description:
  • The Freshman Survey and the College Senior Survey. The sample includes 3,670 students from 217 institutions that indicated they intended to major in a STEM field. 1,634 of these students were underrepresented minorities.
  • The dependent variable used in this study was dichotomous and represented whether students who graduated or were still enrolled after 4 years of college had followed through with their first-year intentions to pursue a degree in a STEM field, or whether they switched majors and completed or continued to pursue a degree in a non-STEM field.
  • The selected variables included student demographics and background characteristics, college experiences, and institutional characteristics. Student-level characteristics were grouped into several blocks to aid analysis and interpretation. Specifically, the authors grouped student background characteristics into (a) demographics (race, sex and socioeconomic status [as proxied by mother’s education level]); (b) high school academic preparation (grades, SAT score, high school course taking patterns), and (c) other pre-college characteristics (including degree aspirations, concern about financing higher education, and student assessments of their academic and social strengths).
  • Finally, all college experiences were included as one block, and within this block there were measures corresponding to students’ interaction with faculty, assessments of the institutional climate, psychosocial concerns, and social and academic integration and involvement. In addition to the student-level variables, institution-level variables were also modeled. These included institutional type and control (4-year/university, public/private), institutional selectivity (measured by the average math and verbal SAT score of entering freshmen), percent of students majoring in STEM fields, structural diversity (percent of student body that is Black, Native American or Latino/a), whether an institution is a historically Black college or university (HBCU), proportion of students receiving financial aid and/or federal aid, and institutional size (as measured by total undergraduate FTE).
  • Because the longitudinal response rate for the TFS-CSS sample was only 23%, the authors calculated and applied response weights to the data to adjust for any non-response bias that might be present. The aim of this weighting was to adjust the CSS sample of respondents to resemble the original population targeted by the CSS that is, the TFS participants. All analyses performed for this study were conducted using weighted data.
Theoretical Framework:
Relevance:Links individual factors and its impacts on STEM. Also looks at college composition and its impacts on STEM.
Archives: K-16 STEM Abstracts