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Standardized Testing for Diverse Talent Identification: A Framework to Address Geographical Bias in Standardized Testing and Increase Diversity in College Admissions in the Post-Affirmative Action/Race-Neutral Admissions Era

Author

Listed:
  • Manuel S. González Canché

    (University of Pennsylvania)

  • Kaiwen Zheng

    (Harvard University)

  • Yantao Song

    (University of International Business and Economics)

  • Yunhao Liang

    (Shanghai American School)

Abstract

Despite the emergence of test-optional policies, standardized admission tests continue shaping the college composition and financial aid prospects of hundreds of thousands of students. This is concerning for the following reasons: (a) standardized test results have historically favored test-takers from wealthier and majority backgrounds, (b) test optional policies have prominently not translated into more diverse student bodies, and (c) on June 29, 2023, the Supreme Court ruled unconstitutional to consider race and ethnicity in college admission decisions. All of which threatens student body diversity in the United States. With this context in mind, this study contributes to this literature in three ways. Methodologically, based on spatial econometrics, neighborhood effects, and concentrated advantage/disadvantage literatures, and employing two different analytic samples, one at the state level and one national in scope, the study demonstrates that variations in test performance are explained by test-takers' spatially contextualized circumstances beyond their individual-level characteristics. Substantively, the study identifies relevant place-based, geographical predictors of performance on standardized tests. Pragmatically, our study offers researchers and admission officers with a tool that enables the easy identification of outstanding, qualified test-takers whom, despite experiencing life in places with high levels of socioeconomic hardship, mastered these tests, statistically outperforming in doing so their peers who grew up in the same neighborhoods. From this perspective, our proposed framework offers a feasible solution to the challenge of locating what Hoxby and Avery referred to as the hidden supply of high-achieving, low-income students. Accordingly, the overarching goal of this study aligns with the notion of talent maximization/human capitalization by providing a reproducible tool (replication code access: https://cutt.ly/3wvv2kgr ) to identify high achieving students located in at risk areas. Since the identification framework of outstanding test-takers discussed in this study does not consider race or ethnicity, it does not violate the Supreme Court’s ruling that prevents using race and ethnicity for admission decisions. Remarkably, our proposed high-achieving, low-income students detection framework does identify standardized test-takers living in highly socioeconomic and ethnic diverse neighborhoods (see https://cutt.ly/hwvraSQD ), which if recruited by admission officers and offered financial aid, may contribute to making the selection process more equitable, fairer, and may ultimately translate into more diverse student bodies.

Suggested Citation

  • Manuel S. González Canché & Kaiwen Zheng & Yantao Song & Yunhao Liang, 2025. "Standardized Testing for Diverse Talent Identification: A Framework to Address Geographical Bias in Standardized Testing and Increase Diversity in College Admissions in the Post-Affirmative Action/Rac," Research in Higher Education, Springer;Association for Institutional Research, vol. 66(2), pages 1-35, March.
  • Handle: RePEc:spr:reihed:v:66:y:2025:i:2:d:10.1007_s11162-024-09824-4
    DOI: 10.1007/s11162-024-09824-4
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