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

GILBOURN, David, WARMINGTON, Paul and DEMACK, Sean (2017). QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics. Race, Ethnicity and Education. (In Press)

[img] PDF (18 month embargo)
Demack-QuantCrit(AM).pdf - Accepted Version
Restricted to Repository staff only
Available under License All rights reserved.

Download (269kB)
[img] PDF (18 month embargo)
Demack-QuantCrit(Fig1and2).pdf - Supplemental Material
Restricted to Repository staff only
Available under License All rights reserved.

Download (95kB)
[img] PDF (18 month embargo)
Demack-QuantCrit(Table1).pdf - Supplemental Material
Restricted to Repository staff only
Available under License All rights reserved.

Download (111kB)

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.

Item Type: Article
Research Institute, Centre or Group: Sheffield Institute of Education
Depositing User: Jill Hazard
Date Deposited: 06 Sep 2017 15:20
Last Modified: 22 Sep 2017 00:18
URI: http://shura.shu.ac.uk/id/eprint/16657

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics