Predicted Data Analysis: The Impacts of Implicit Bias on Evaluations of Graduate Student
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Despite universities attempting to adopt more EDI (equity, diversity, and inclusion) friendly policies and
increasing encouragement for women and marginalized groups to pursue STEM, there continues to be
underrepresentation of these individuals in STEM fields and tenure-track faculty positions. This has led to
research exploring the impacts of implicit bias on the advancement of marginalized groups in STEM. This
research will be focused on how the implicit biases of an evaluator can impact the evaluation of a science
graduate student applicant. As the preliminary trial and final experiment has not yet taken place, this paper will
be using fake data sets to show how the data from this experiment will be analyzed. The data is partially
inspired from similar past research and will display relevant concerns in applicant evaluation. This paper will
explore the potential impacts of gender bias, racial bias, and intersectionality on graduate student applicants,
while explaining the methods and expectations of an experiment designed to evaluate how sharing personal
information on applications can affect the evaluation of the applicant.
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http://purl.org/coar/resource_type/c_6670
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en