English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Researchers often frame quantitative research as objective, but every step indata collection and analysis can bias findings in often unexamined ways. Inthis investigation, we examined how the process of selecting variables toinclude in regression models (model specification) can bias findings aboutinequities in science and math student outcomes. We identified the four mostused methods for model specification in discipline-based education researchabout equity: a priori, statistical significance, variance explained, andinformation criterion. Using a quantitative critical perspective that blendsstatistical theory with critical theory, we reanalyzed the data from a priorpublication using each of the four methods and compared the findings from each.We concluded that using information criterion produced models that best alignedwith our quantitative critical perspective's emphasis on intersectionality andmodels with more accurate coefficients and uncertainties. Based on thesefindings, we recommend researchers use information criterion for specifyingmodels about inequities in STEM student outcomes.

How statistical model development can obscure inequities in STEM student outcomes