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Abstract We investigated the intersectional nature of race/racism and gender/sexism in broad scale inequities in physics student learning using a critical quantitative intersectionality. To provide transparency and create a nuanced picture of learning, we problematized the measurement of equity by using two competing operationalizations of equity:Equity of IndividualityandEquality of Learning. These two models led to conflicting conclusions. The analyses used hierarchical linear models to examine student's conceptual learning as measured by gains in scores on research‐based assessments administered as pretests and posttests. The data came from the Learning About STEM Student Outcomes' (LASSO) national database and included data from 13,857 students in 187 first‐semester college physics courses. Findings showed differences in student gains across gender and race. Large gender differences existed for White and Hispanic students but not for Asian, Black, and Pacific Islander students. The models predicted larger gains for students in collaborative learning than in lecture‐based courses. The Equity of Individuality operationalization indicated that collaborative instruction improved equity because all groups learned more with collaborative learning. The Equality of Learning operationalization indicated that collaborative instruction did not improve equity because differences between groups were unaffected. We discuss the implications of these mixed findings and identify areas for future research using critical quantitative perspectives in education research.
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Themed Issue: Best practices for specifying statistical models in STEM equity research
Researchers often frame quantitative research as objective, but every step in data collection and analysis can bias findings in often unexamined ways. In this investigation, we examined how the process of selecting variables to include in regression models (model specification) can bias findings about inequities in science and math student outcomes. We identified the four most commonly used methods for model specification in discipline-based education research about equity: a priori, statistical significance, variance explained, and information criterion. Using a quantitative critical perspective that blends statistical theory with critical theory, we reanalyzed the data from a prior publication (Van Dusen & Nissen, 2020) using each of the four methods and compared the findings from each. We concluded that using information criterion produced models that best aligned with our quantitative critical perspective’s emphasis on intersectionality and models with more accurate coefficients and uncertainties. Based on these findings, we recommend researchers use information criterion for specifying models about inequities in STEM student outcomes.
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- Award ID(s):
- 1928596
- PAR ID:
- 10327863
- Date Published:
- Journal Name:
- Journal of Women and Minorities in Science and Engineering
- Volume:
- 28
- Issue:
- 3
- ISSN:
- 1072-8325
- Page Range / eLocation ID:
- 27-58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1615/JWomenMinorScienEng.2022036220
