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.
Acknowledgements
This paper draws on research conducted for the project ‘Race, Racism and Education: inequality, resilience and reform’, funded by the 2013 Research Award by the Society for Educational Studies (SES). We are especially grateful to our advisory group for their support and advice; especially Sir Keith Ajegbo, Hilary Cremin, Diane Rutherford, Sally Tomlinson, and Joy Warmington. We are indebted to the editors of this special issue for their detailed comments on the text and to our colleague Claire E. Crawford for her help with final revisions.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Society for Educational Studies, followed by the [2013 National Award].
Notes
1. Neo-liberalism refers to the dominant policy lens in contemporary states such as the US and UK. The approach emphasizes an individualized view of the world and assumes that the free market offers the most efficient and fairest means of meeting societal needs (Lauder et al. Citation2006). Neoliberalism typically assumes that success reflects individual merit and hard work, and that private provision is inherently superior to public. Neoliberalism often works through color-blind language that dismisses race-conscious criticism as irrelevant, meaningless and/or inflammatory (see Gillborn Citation2014).
2. Data here is taken from the Longitudinal Study of Young People in England (LSYPE1). These students entered university in 2008/09 and 2009/10. For further details on the LSYPE see UCL Institute of Education (Citationn.d.).
3. These are the ethnic group categories used in the UK census and, consequently, in most academic research in the UK; the combination of race/color and national identifiers is far from satisfactory and can be misleading. For example, the majority of children in each of these groups were born in the UK and enjoy full UK citizenship (see Office for National Statistics (ONS) Citation2012).
4. This is based on the ‘odds ratio’ (also known as ‘cross-product ratio’) calculated by comparing the odds of success for White students compared with the odds of success for Black students (see Connolly Citation2007, 107–8).
5. On 9 August 2016 a google search for the phrase ‘big data’ returned ‘about 296,000,000 results’. A similar search performed three years earlier returned 158,000,000 results.
6. Verbatim transcription from the podcast ‘Start the Week’, BBC Radio 4 (Citation2013).