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Undergraduate Research Experience and Post-graduate Achievement Among Students from Underrepresented Groups in STEM

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Abstract

Racial and ethnic disparities in STEM achievement are associated with weaker economic growth, greater social inequalities, and narrower parameters of scientific inquiry. Extant research suggests that undergraduate research experiences (URE) can reduce those disparities by enhancing perceptions of belonging and scientific self-efficacy among students from underrepresented groups. However, to date, very few studies have examined the relationship between URE and post-baccalaureate educational achievement gains among such students and those that have tend to be limited in terms of causal leverage and generalizability. In this study, we aim to make progress by analyzing data from the California State University system’s longstanding Louis Stokes Alliance for Minority Participation (CSU-LSAMP) program. Applying a quasi-experimental research design and drawing upon a large and representative sample of students whom we tracked over time, we observe that URE is strongly associated with post-baccalaureate enrollment and graduation in STEM disciplines among students from underrepresented backgrounds.

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Notes

  1. The California State University, Impact of the CSU/Diversity. Available at: https://www2.calstate.edu/impact-of-the-csu/diversity.

  2. California State University Louis Stokes Alliance for Minority Participation STEM Pathways and Research Alliance, Year Two Report, June 2020, Institute for Social Research. Available at: https://www.csus.edu/college/natural-sciences-mathematics/csu-lsamp/_internal/_documents/csu-lsamp-spara-year-two-report.pdf.

  3. For a full CSU-LSAMP program description, see the program’s website: https://www.csus.edu/college/natural-sciences-mathematics/csu-lsamp/program-goals.html.

  4. We were able to obtain ERS data for 80% of CSU-LSAMP participants and NSC records for 68% of CSU-LSAMP participants. ERS data could not be obtained for CSU-LSAMP participants whose WebAMP records were missing SSN. Students were matched to NSC data using name and birthdate. Students can also decline to share their data with the NSC, which accounts for the lower match rate with NSC data.

  5. We use a simplified measure of URE, rather than one that measures differences in duration of participation (e.g., multiple years vs. one), because the matching method we employed is much more complicated if attempting to equalize three groups (non-URE, 1 year of URE, 2 or more years of URE) instead of just two, making us much more confident in the stability of the estimates when using this simplified procedure. However, it is worth pointing out that in preliminary statistical analyses in which we simply include the matching variables as model covariates, we did include a 3-point measure of URE that distinguishes those who participated in such research activities for only 1 year and those who participated for 2 or more years. Those models reveal that, generally speaking, such additional URE “dosage” slightly strengthens the size of the coefficients associated with URE participation. Thus, the estimates we report below may be considered conservative.

  6. As a robustness test, we also estimated models using the nearest neighbor method of matching. We did so repeatedly, specifying that the procedure use 1 and 10 “nearest neighbors” to match. The results we obtained look highly similar, in terms of both substantive and statistical significance, to those we report here.

  7. As another robustness check, in alternative model estimations, we used the complementary log-log estimator in light of the varying degrees of skew associated with our outcome variables. The results did not meaningfully differ, either substantively or statistically, in those models, so we report the probit results because probit regression is familiar to a broader audience of readers.

  8. Rather than an HLM model assuming random campus effects, in alternative models, we included “fixed effects” dummy variables for all the campuses to account for any selection bias associated with unobserved campus-level variables. Because doing so added multicollinearity to the models, causing some of the covariates to be dropped, we do not report those results here. However, importantly, the inclusion of campus fixed effects did not substantially alter any of the results we report below.

  9. Additional analyses reveal that URE is also associated with increases in graduation rates in non-STEM fields, but the relationship is smaller.

  10. Unfortunately, limitations in data availability precluded us from analyzing post-graduate enrollment in STEM fields, specifically. Specifically, NSC enrollment data does not consistently provide the level of enrollment or discipline that information is only reflected in NSC records when a degree is awarded. If we had, we might have observed an even stronger relationship, given the other patterns of results we report here.

  11. Additional analyses reveal that URE is also associated with post-graduate degree attainment in non-STEM fields, though the relationship is smaller.

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Authors and Affiliations

  1. School of Public Affairs, American University, 4400 Massachusetts Ave. NW, Washington, DC, 20016, USA

    David C. Barker & Dave E. Marcotte

  2. Institute for Social Research, California State University, Sacramento, 304 S Street, Suite 333, Sacramento, CA, 95811, USA

    Valory Messier, Lisa Hammersley & Semarhy Quinones-Soto

Authors
  1. David C. Barker
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  2. Valory Messier
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  3. Dave E. Marcotte
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  4. Lisa Hammersley
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  5. Semarhy Quinones-Soto
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Corresponding author

Correspondence to Dave E. Marcotte.

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Appendices

Appendix 1

This section describes all variables used in the analysis. Table 1 displays descriptive statistics for all variables.

Student Characteristics

  • Undergraduate research experience: undergraduate research experience outside of the classroom under the guidance of a faculty mentor. This variable is coded 0|1, with 1 representing one or more years of URE.

  • STEM bachelor’s degree: this variable is coded 0|1, with 1 representing completion of a STEM bachelor’s degree.

  • Post-baccalaureate enrollment: this variable is coded 0|1, with 1 representing enrollment following completion of a bachelor’s degree. NSC enrollment data does not consistently provide the level of enrollment or discipline; that information is only reflected in NSC records when a degree is awarded.

  • STEM graduate degree: This variable is coded 0|1, with 1 representing completion of a STEM masters or doctoral degree.

  • Major: all CSU-LSAMP students are STEM majors (or considering a STEM major). Dummy variables (0|1) were created for biology, math, physical sciences, computer science, engineering/engineering technologies, and natural resources and conservation. All other STEM majors were used as the reference category.

  • Gender: 0= male, 1 =female.

  • Pell eligibility: dummy variable is coded as 0 = not Pell eligible and 1 = Pell eligible.

  • Age at CSU entry: continuous age at CSU entry.

  • Race/ethnicity: dummy variables (0|1) were created for all race/ethnicity categories. Hispanic students of all races are coded as Hispanic. The non-Hispanic multiracial UR category includes students who identified themselves as multiracial, where at least one race category was Black, Native American, or Pacific Islander. The non-Hispanic multiracial non-UR category includes students who identified themselves as White and Asian. Non-Hispanic Whites were used as the reference category. Six non-Hispanic multiracial non-UR transfer students were dropped from the transfer student sample as none had participated in URE, and no weights could be generated for them.

  • Academic preparedness: for traditional students, SAT scores and HS GPA scores were converted to standard deviation units (with a mean of zero and a standard deviation of 1). This was accomplished by simply subtracting the mean from each observation in the variable and then dividing it by its standard deviation. Then, these two variables were summed into an index. Then, the resulting two-item index was converted to a 0-1 scale, by adding the lowest value of the variable to each observation (that brought the lowest observation up to 0) and then dividing by the highest value. For transfer students, transfer GPA was used, as SAT scores and HS GPA were not available.

  • Class at CSU entry: dummy variables (0|1) were created for each class level at entry (freshman, sophomore, junior, and senior).

Campus Variables

  • CSU-LSAMP program size: total number of program participants at each campus 2021.

  • Total enrollment: total campus enrollment in fall 2020.

  • Students in off-campus housing: proportion of students in off-campus housing.

  • Acceptance rate: proportion of students who applied who were admitted to each campus.

  • Urbanicity: dummy variables (0|1) were created for rural, suburban, and urban campuses. Rural is used as the reference category.

  • Program emphasis (URE implementation): CSU-LSAMP programs can select one of three program emphases: academic (focused student success), professionalization (focused on graduate school preparation and URE), and dual emphasis (both academic and professionalization). Only one campus had an academic focus, California Maritime Academy, a small program with very few research participants. This campus was grouped with the dual campuses in the reference category.

  • Proportion of White faculty: proportion of full-time instructional staff who were White in 2020, data comes from the IPEDS survey.

  • Proportion of White students: proportion of students who were White in 2020.

Table 1 Means and standard deviations for all variables
Full size table

Appendix 2. Balance tables

Table 2 Unweighted and weighted means for URE non-participants and participants: traditional students
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Table 3 Unweighted and weighted means for URE non-participants and participants: transfer students
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Appendix 3. Full results

Table 4 URE and STEM educational achievement: traditional students
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Table 5 Full probit model results for transfer students
Full size table

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Barker, D.C., Messier, V., Marcotte, D.E. et al. Undergraduate Research Experience and Post-graduate Achievement Among Students from Underrepresented Groups in STEM. Journal for STEM Educ Res (2023). https://doi.org/10.1007/s41979-023-00107-8

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