Applying Critical Race Quantitative Intersectionality

• In Event: 34.043 - "Powerful, Empirically Driven, and Multimodal Narratives...and Statistics": Expanding the Reach of Critical Race Theory

Abstract

The Critical Race Intersectional Think Tank (CRITT) is a praxis-based collective created by scholars trained in critical race-theory working to innovate methodologies to produce structural, institutional, cultural, and personal transformative ruptures in and outside of academia. Delgado Bernal and Alemán (2017) describe these ruptures as “incidents, interactions, experiences and moments that expose and interrupt pervasive coloniality and structural inequities” (p. 5; emphasis added). The authors describe a methodological project, that is also ontological in their effort to conjoin theory, action, and reflection.
Using critical race theory as a framework, the authors utilize Critical Race Quantitative Intersectionality (CRQI) to expose regional disparities captured by localized educational pipelines. After describing the linkages between CRT and CRQI, the authors utilize testimonios to reveal structural oppression not captured by a strict reliance on traditional quantitative analyses for curricular outcomes. As CRITT, their analytical process includes preliminary analysis of quantitative data, examination of assumptions, the construction and sharing of their own testimonios, and community building within academia. The authors assert that structural oppression is not captured by a strict reliance on traditional quantitative analyses alone. Analyzing enumerated data through statistical software is useful for demonstrating important patterns that help researchers make meaning, the meaning and nature of those patterns is further elucidated by rich, descriptive narratives that have an intentional liberatory purpose, as in testimonies.
For example, the authors mine data from the American Community Survey (ACS), which is collected by the United States Census Bureau annually and aggregated in 3-year and 5-year datasets. Using DataFerret they collect and customize microdata files from the Public Use Micro Sample (PUMS) that covers a wide range of topics across the full scope of population and housing information collected in the ACS. DataFerret allows the authors to create intersectional conceptual models of educational outcomes for diverse people across the country. More specifically, they use self-identified labels that reflect established social constructions as well as institutional and ideological relationships to capture the educational attainment data reported by the US Census. We successively disaggregate this data using a combination of these constructions: race, ethnicity, citizenship, class, sex, and geography.
In addition to progressive disaggregation of homogenizing data, their work involves reconceptualizing how data can capture relationships and experiences within specific material conditions, instead of who it purports to represent. For example, the authors group people who earn a first-grade education with those who attend twelve years of schooling but do not get a diploma, into a category identified as “high school pushouts.” This methodological decision not only conforms to established pipeline methods, but also rejects deficit based “dropouts” discourse.
CRQI is much more about rethinking what is measured, how it is measures and what those measures mean. CRQI is a methodological research approach that involves many acts of resistance to normative and normalizing research processes, relationships, and analyses. It rejects hierarchical research relationships, disconnected involvement in the analytical processes, and privileging of dominant ideological constructs related to quantitative research.

Authors

• Alejandro Covarrubias, University of California - Los Angeles

• Pedro E. Nava, Mills College

• Argelia Lara, Mills College

• Rebeca Burciaga, San José State University