Dropping the GRE, keeping the GRE, or GRE-optional admissions? Considering tradeoffs and fairness

Daniel A. Newman, Chen Tang, Q. Chelsea Song, Serena Wee

Research output: Contribution to journalArticlepeer-review

Abstract

In considering whether to retain the GRE in graduate school admissions, admissions committees often pursue two objectives: (a) performance in graduate school (e.g., admitting individuals who will perform better in classes and research), and (b) diversity/fairness (e.g., equal selection rates between demographic groups). Drawing upon HR research (adverse impact research), we address four issues in using the GRE. First, we review the tension created between two robust findings: (a) validity of the GRE for predicting graduate school performance (rooted in the principle of standardization and a half-century of educational and psychometric research), and (b) the achievement gap in test scores between demographic groups (rooted in several centuries of systemic racism). This empirical tension can often produce a local diversity-performance tradeoff for admissions committees. Second, we use Pareto-optimal tradeoff curves to formalize potential diversity-performance tradeoffs, guiding how much weight to assign the GRE in admissions. Whether dropping the GRE produces suboptimal admissions depends upon one’s relative valuation of diversity versus performance. Third, we review three distinct notions of test fairness—equality, test equity, and performance equity—which have differing implications for dropping the GRE. Finally, we consider test fairness under GRE-optional admissions, noting the missing data problem when GRE is an incomplete variable. Supplemental data for this article is available online at.

Original languageEnglish (US)
Pages (from-to)43-71
Number of pages29
JournalInternational Journal of Testing
Volume22
Issue number1
DOIs
StatePublished - 2022

Keywords

  • GRE
  • Pareto-optimal weighting
  • diversity
  • test fairness

ASJC Scopus subject areas

  • Social Psychology
  • Education
  • Modeling and Simulation

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