Critical Race Quantitative Intersectionality

An Anti-Racist Research Paradigm that Refuses to “Let the Numbers Speak for Themselves”

Authored by: Covarrubias Alejandro , Vélez Verónica

Handbook of Critical Race Theory in Education

Print publication date:  March  2013
Online publication date:  September  2013

Print ISBN: 9780415899956
eBook ISBN: 9780203155721
Adobe ISBN: 9781136581410

10.4324/9780203155721.ch20

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Abstract

“Show me the data!” “The numbers” are omnipotent and often the only valid sources of data for many academicians, policymakers, and those most influential in the field of education (Kamil, 2004). Both private and public funding sources demand that the “hard facts” of outcomes are provided through quantitative data (Simonson, 2005). Politely, struggling non-profit agencies and public institutions comply. We give this data so much power, celebrating its purported objectivity and neutrality, that oftentimes we forget data—any data—is shaped by the sociopolitical context within which it arose, by the scientists who “discovered” it (Gould, 1996; Said, 1978; Zuberi, 2001). The over-reliance in mainstream policy and research arenas on quantitative data leads many to proclaim, “Let the numbers speak for themselves.” Our training as critical race scholars has taught us that the numbers never “speak for themselves” and that, in fact, the numbers are given voice largely by the theoretical underpinnings upon which they rest. Bonilla-Silva and Zuberi (2008), for example, argue that claims of impartiality regarding statistical research fail to recognize its historical roots in white supremacy and the eugenics movement. They contend that “statistical analysis was developed alongside a logic of racial reasoning. That the founder of statistical analysis also developed a theory of [w]hite supremacy is not an accident” (Bonilla-Silva & Zuberi, 2008, p. 5). Consequently, a white logic has formed in quantitative methods, blinding social scientists in their contemporary research regarding race, especially its causal findings and its applications (Bonilla-Silva & Zuberi, 2008). The result? Rather than challenge racial stratification, social science becomes the justification for it. Framed as “objective” and “neutral,” this misguided research has gone on to shape educational policy, allocate resources, and guide programming and practices that impact the education of low-income communities of color. We assert that a critical race quantitative intersectionality (CRQI) in the field of education challenges the lasting legacy of an erroneous, and arguably racist, application of statistical methods in the social sciences and expands the utility and transformative potential of critical race theory (CRT).

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