Beyond the numbers: a reflexive journey of interrogating racial categorization in QuantCrit

Quantitative methodologies have historically overlooked the complexities of race, yet critical quantitative methods and QuantCrit have emerged to challenge traditional approaches. This paper explores a faculty member’s and a doctoral student’s journeys into QuantCrit, emphasizing the third tenet: “Categories are neither natural nor given.” The authors critique their own work, engage with existing literature, and propose future directions, including artificial intelligence’s role in demographic classification and the potential for intersectional quantitative analyses. By interrogating rigid categorization and advocating for methodological justice, this paper advances the conversation on racial equity in quantitative research. • Critiques racial categorization in quantitative research through QuantCrit. • Focuses on the QuantCrit tenet: categories are neither natural nor given. • Shares reflexive journeys of a faculty member and doctoral student. • Proposes AI and intersectional analyses for methodological justice.

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Publication Details

Journal
Current Opinion in Behavioral Sciences
Published
2025-08-30
DOI
https://doi.org/10.1016/j.cobeha.2025.101591
Primary Topic
Critical Race Theory in Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Beyond the numbers: a reflexive journey of interrogating racial categorization in QuantCrit

Lucy Arellano, Catherine A Fitch
Current Opinion in Behavioral Sciences
Critical Race Theory in Education
article

Beyond the numbers: a reflexive journey of interrogating racial categorization in QuantCrit

Lucy Arellano, Catherine A Fitch
article en

Abstract

Quantitative methodologies have historically overlooked the complexities of race, yet critical quantitative methods and QuantCrit have emerged to challenge traditional approaches. This paper explores a faculty member’s and a doctoral student’s journeys into QuantCrit, emphasizing the third tenet: “Categories are neither natural nor given.” The authors critique their own work, engage with existing literature, and propose future directions, including artificial intelligence’s role in demographic classification and the potential for intersectional quantitative analyses. By interrogating rigid categorization and advocating for methodological justice, this paper advances the conversation on racial equity in quantitative research. • Critiques racial categorization in quantitative research through QuantCrit. • Focuses on the QuantCrit tenet: categories are neither natural nor given. • Shares reflexive journeys of a faculty member and doctoral student. • Proposes AI and intersectional analyses for methodological justice.

Current Opinion in Behavioral SciencesVol. 66
University of California, Santa Barbara (US)
Reduced inequalities
Openalex Percentile: Top 69%
Critical Race Theory in Education
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