  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.
