QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics

Abstract

Quantitative research enjoys heightened esteem among policy-makers, media and the general public. Whereas qualitative research is frequently dismissed as subjective and impressionistic, statistics are often assumed to be objective and factual. We argue that these distinctions are wholly false; quantitative data is no less socially constructed than any other form of research material. The first part of the paper presents a conceptual critique of the field with empirical examples that expose and challenge hidden assumptions that frequently encode racist perspectives beneath the façade of supposed quantitative objectivity. The second part of the paper draws on the tenets of Critical Race Theory (CRT) to set out some principles to guide the future use and analysis of quantitative data. These 'QuantCrit' ideas concern (1) the centrality of racism as a complex and deeply-rooted aspect of society that is not readily amenable to quantification; (2) numbers are not neutral and should be interrogated for their role in promoting deficit analyses that serve White racial interests; (3) categories are neither 'natural' nor given and so the units and forms of analysis must be critically evaluated; (4) voice and insight are vital: data cannot 'speak for itself' and critical analyses should be informed by the experiential knowledge of marginalized groups; (5) statistical analyses have no inherent value but can play a role in struggles for social justice.

Associations between learning assistants, passing introductory physics, and equity: A quantitative critical race theory investigation

“…For example, Disability CRT (DisCrit) [58][59][60] has been taken up by scholars to examine the intersection of racism and disabilities. To analyze and interpret our findings, we used a Quantitative CRT (QuantCrit) [25,61,62] perspective.…”

“…Critical research has historically used qualitative approaches to investigate the lived experiences of marginalized people and the social processes that create racist, sexist, and classist power structures [61][62][63]. QuantCrit emerged as a quantitative perspective [25] aligned with the core principles of critical research.…”

Problematising the (in)visibility of racialized and gendered British Chineseness in youth health and physical cultures

“…individual change across time), it is less helpful in terms of deconstructing categorical thinking and discovering the nuances, contradictions, and everyday experiences of physical education, sport, leisure, and physical activity within Chinese diaspora youth (Flintoff & Webb, 2012;Pang, 2018). As Gillborn, Warmington, and Demack (2018) highlighted, although racialised categories in crude measures of educational achievements can support anti-racist work by highlighting structural inequalities, such research can also work against minority groups and perpetuate essentialised bodily differences. Exploring the experiences of Chinese diaspora youth in physical cultures could serve to complement survey-based research and provide relational understanding of the meanings and engagement of young people.…”

Linguistic and racial re-formation of indigenous Mexicans’ languages and races in Florida Heartland K-12 schools

“…Additionally, the QuantCrit approach to analysis (Gillborn, Warmington, and Demack 2018) allows the articulation of how racial re-formation occurs. QuantCrit applies Critical Race Theory (CRT) to quantitative data in order to question how that data came to be.…”

“…CRT has challenged claims of neutrality and objectivity in educational data for decades (Ladson-Billings and Tate 1995). Gillborn, Warmington, and Demack (2018) argue that research that has a goal of highlighting racial inequality often only presents correlations of race with negative outcomes, such as low achievement, rather than explaining how historical and current discrimination bare such inequality. The principles of QuantCrit are "the centrality of racism, [that] numbers are not neutral, [that] categories are neither 'natural' nor given: for 'race' read 'racism', [in] voice and insight: data cannot 'speak for itself', [and that we can and should be] using numbers for social justice" (Gillborn, Warmington, and Demack 2018: 169).…”