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Patterns of Gendered Performance Differences in Large Introductory Courses at Five Research Universities

Version
1
Resource Type
Dataset
Creator
  • Matz, Rebecca (Michigan State University)
  • Koester, Benjamin (University of Michigan)
  • Fiorini, Stefano (Indiana University)
  • Grom, Galina (University of Michigan)
  • Shepard, Linda (Indiana University)
  • Stangor, Charles (University of Maryland)
  • Weiner, Brad (University of Minnestora)
  • mckay, timothy (University of Michigan)
Publication Date
2019-01-04
Description
  • Abstract

    Abstract: Significant gendered performance differences are signals of systemic inequity in higher education. Understanding of these inequities has been hampered by the local nature of prior studies; consistent measures of performance disparity across many disciplines and institutions have not been available. Here, we report the first wide-ranging, multi-institution measures of gendered performance difference, examining more than a million student enrollments in hundreds of courses at five universi- ties. After controlling for factors that relate to academic performance using optimal matching, we identify patterns of gen- dered performance difference that are consistent across these universities. Biology, chemistry, physics, accounting, and economics lecture courses regularly exhibit gendered performance differences that are statistically and materially significant, whereas lab courses in the same subjects do not. These results reinforce the importance of broad investigation of performance disparities across higher education. They also help focus equity research on the structure and evaluative schemes of these lecture courses.
Availability
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Update Metadata: 2019-02-15 | Issue Number: 1 | Registration Date: 2019-02-15

Matz, Rebecca; Koester, Benjamin; Fiorini, Stefano; Grom, Galina; Shepard, Linda et. al. (2019): Patterns of Gendered Performance Differences in Large Introductory Courses at Five Research Universities. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E108502V1