How Statistical Model Development Can Obscure Inequities In Stem Student Outcomes

dc.contributor.author Van Dusen, Ben
dc.contributor.author Nissen, Jayson
dc.contributor.department School of Education
dc.date.accessioned 2024-04-22T15:18:56Z
dc.date.available 2024-04-22T15:18:56Z
dc.date.issued 2022
dc.description.abstract 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 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 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.
dc.description.comments This accepted article is published as Van Dusen, B., Nissen, J., How Statistical Model Development Can Obscure Inequities In Stem Student Outcomes. Annual Review of heat Transfer. 2022; 27-58. https://doi.org/10.1615/JWomenMinorScienEng.2022036220. Posted with permission.
dc.identifier.issn 1049-0787
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/YvkAgkez
dc.language.iso en
dc.publisher Begell House
dc.source.uri https://doi.org/10.1615/JWomenMinorScienEng.2022036220 *
dc.subject.disciplines DegreeDisciplines::Education::Higher Education
dc.subject.keywords QuantCrit
dc.subject.keywords model specification
dc.subject.keywords equity
dc.subject.keywords quantitative methods
dc.subject.keywords regression
dc.title How Statistical Model Development Can Obscure Inequities In Stem Student Outcomes
dc.type Article
dspace.entity.type Publication
relation.isAuthorOfPublication 3fe9621a-07ea-41be-a7e4-23780d4a22f5
relation.isOrgUnitOfPublication 385cf52e-6bde-4882-ae38-cd86c9b11fce
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