QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics
GILBOURN, David, WARMINGTON, Paul and DEMACK, Sean http://orcid.org/0000-0002-2953-1337
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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.
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QuantCrit: education, policy, ‘Big Data’ and principles for a critical race
theory of statistics
David Gillborn, University of Birmingham, UK. [email protected]
Paul Warmington, University of Warwick, UK. [email protected]
Sean Demack, Sheffield Hallam University, UK. [email protected]
Keywords:
critical race theory; quantitative research methods; statistics; race; racism; education policy;
Big Data.
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. 1
INTRODUCTION
1988
St. George’s Hospital Medical School has been found guilty by the Commission for Racial
Equality of practising racial and sexual discrimination in its admissions policy … a computer
program used in the initial screening of applicants for places at the school unfairly
discriminated against women and people with non-European sounding names… By 1988 all
initial selection was being done by computer ... Women and those from racial minorities had a
reduced chance of being interviewed independent of academic considerations. (Lowry &
Macpherson 1988)
2016
…judges, police forces and parole officers across the US are now using a computer program
to decide whether a criminal defendant is likely to reoffend or not. The basic idea is that an
algorithm is likely to be more ‘objective’ and consistent than the more subjective judgment of
human officials ... But guess what? The algorithm is not colour blind. Black defendants who
did not reoffend over a two-year period were nearly twice as likely to be misclassified as
higher risk compared with their white counterparts; white defendants who reoffended within
the next two years had been mistakenly labelled low risk almost twice as often as black
reoffenders. (Naughton 2016).
These quotations describe how calculations made by computers, assumed by definition to be objective
and free from human bias, not only reflected existing racist stereotypes but then acted upon those
stereotypes to create yet further racial injustice. The incidents are separated by an ocean and almost 30
years; the first refers to an English medical school, the second to a program used across the US. But
the news coverage generated by the events is strikingly similar. In both cases there was a sense of
amazement that computer calculations could make such gross and racially patterned errors. In the US
example the reporters who found the problem note that ‘even when controlling for prior crimes, future
recidivism, age, and gender, black defendants were 77 percent more likely to be assigned higher risk
scores than white defendants’ (Larson, Mattu, Kirchner & Angwin 2016). A UK news story on
the findings was entitled ‘Even algorithms are biased against black men’ (Naughton 2016
emphasis added). The surprise that accompanies such findings reflects the central problem
that we address in this paper; we argue that, far from being surprised that quantitative
calculations can re-produce human bias and racist stereotypes, such patterns are entirely
predictable and should lead us to treat quantitative analyses with at least as much caution as
when considering qualitative research and its findings. Computer programs, the ‘models’ that 2
they run, and the calculations that they perform, are all the product of human labour. Simply
because the mechanics of an analysis are performed by a machine does not mean that any
biases are automatically stripped from the calculations. On the contrary, not only can
computer-generated quantitative analyses embody human biases, such as racism, they also
represent the added danger that their assumed objectivity can give the biases enhanced
respectability and persuasiveness. Contrary to popular belief, and the assertions of many
quantitative researchers, numbers are neither objective nor color-blind.
Our Position and the Aims of this Paper
We write from a perspective that foregrounds the need to think critically about how race
inequity is routinely embedded in the everyday mundane realities that shape society, from the
economy, to education, and the academy. The social locations of the authors of this paper
differ in some respects and overlap in others. One of us is biracial (in the current dominant
language of UK census categories, Black Caribbean/White British); two are White British.
All of us are British-born male academics from working-class family backgrounds. As
scholars, we have converged around our use of Critical Race Theory (CRT) as a framework
for approaching issues of education and social justice. Our commitment to confronting the
persistence of racism within the socio-educational formation derives from our own personal
experiences of educational inequalities as students in state education and our concerns as
educators/activists - particularly our frustration with the ‘colour-blindness’ that is the default
in British education policy (Gillborn 2008; Warmington 2014). This paper is grounded in
CRT’s understanding that ‘race and races are products of social thought and relations’ and
that racism is non-aberrational (Delgado & Stefancic 2001, 7). In precise terms our position is
one of ‘race ambivalence’ (Leonardo 2011, 675). That is, we understand that while race may
be ‘unreal’ as a scientific category, its ‘modes of existence’ are real and have innumerable
material and social consequences (Leonardo 2005, 409). It is indefensible, therefore, merely
to regard race as a technology of other supposedly more ‘real’ relationships, such as social
class.
In this paper we apply a critical race perspective to the guiding questions that shaped this
special issue of the journal ‘Race Ethnicity and Education’. In particular, we respond to the
editors’ provocation to consider how quantitative methods - long critiqued for their inability
to capture the nuance of everyday experience - might support and further a critical race
agenda in educational research? Our answer is that different methods are appropriate for 3
different aspects of social research and critique. Quantitative methods cannot match
qualitative approaches in terms of their suitability for understanding the nuances of the
numerous social processes that shape and legitimate race inequity. However, quantitative
methods are well placed to chart the wider structures, within which individuals live their
everyday experiences, and to highlight the structural barriers and inequalities that differently
racialized groups must navigate.
Alongside the possible use of quantitative methods to aid a critical race analysis, we are
especially aware that statistics are frequently mobilized to obfuscate, camouflage and even to
further legitimate racist inequities. This paper attempts to show how such misuses occur and
set out a range of CRT principles that can provide a lens through which to read and critique
ostensibly ‘neutral’, ‘objective’ numbers and reporting that, in fact, conceal racist
assumptions. We present our arguments in two main sections, combining a conceptual
critique of the field with empirical examples that expose and challenge the hidden
assumptions that frequently pattern quantitative analyses of race inequity.
First, we look at how numbers are used to disguise racism in education and protect the racist
status quo, that is, a position of White supremacy where the assumptions, interests, fears and
fantasies of White people are placed at the heart of everyday politics and policy-making. We
critique the special status that is wrongly accorded to quantitative data and debunk the truth
claims associated with statistical research. In particular, we show how numbers have been
deployed in recent education policy that claims to address issues of accountability and equity.
Many of the most dangerous aspects of quantitative hyperbole coalesce in the emerging field
of ‘Big Data’, where advocates argue that ‘numbers speak for themselves’ (Anderson 2008)
and human reasoning (and experience) simply get in the way.
The second part of the paper argues that, with appropriate safeguards and reflexivity,
quantitative material has the potential to contribute to a radical project for greater equity in
education. We build upon previous relevant research and go further by explicitly drawing on
classic work in CRT to set out key principles that might usefully guide the use of quantitative
material as part of the wider struggle for racial justice in education.
MAGIC NUMBERS?
CHALLENGING THE SPECIAL STATUS ACCORDED TO QUANTITATIVE DATA 4
Numbers and Truth Claims
Policy-makers, the media and many academics treat quantitative material as if it is fundamentally
different and superior to qualitative data. Numbers are assumed to report ‘the facts’; they are seen as
authoritative, neutral, dispassionate and objective. Indeed, governments do not use numbers merely to
describe the world, they increasingly use statistics as an essential part of the technology by which they
seek to re/shape educational systems. In this way, numbers play a key role in how inequality is
shaped, legitimized and protected. This has been called ‘policy as numbers’ (Rose 1999; Ozga &
Lingard 2007; Rizvi & Lingard 2010):
neo-liberalism has enhanced the significance of numbers and statistics as technologies of
governance, as central to what Power (1997) calls the rise of the ‘audit society’ and what
Neave (1998) has called ‘the evaluative state’. (Lingard 2011, 359) [ 1 ]
Numbers are increasingly used to justify policy priorities and to label teachers, schools, districts, and
even entire countries, as educational successes and failures. National testing programs, such as the No
Child Left Behind (NCLB) reforms in the US and the use of school performance tables in England,
have popularized the idea that numbers can be used to expose (and change) failing schools (Barber
2012; Darling-Hammond, 2007; Gillborn & Youdell 2000). For example, across the globe politicians
and pressure-groups frequently try to make their case by quoting results from PISA (Program of
International Student Assessment) – which is run by the Organisation for Economic Co-operation and
Development (OECD). Prominent examples exist in the States, the UK and Australia (see Lingard,
Creagh & Vass 2012). Countries’ positions in the PISA tables are often cited as if they
unambiguously and accurately represent the relative quality of schooling in different nations (despite
their very different populations and education systems). And yet the commentaries rarely include any
detail about the relatively small samples (less than 200 schools in all but one of the US returns since
2000)(NCES nd); the selective curricular coverage of the tests (in reading, math and science); nor the
fact that students in different countries sometimes take different assessments or miss certain
assessments altogether (Stewart 2013). Despite these severe limitations, the UK government
frequently cites PISA results as evidence of the need for change (cf. DfE 2015, 8) and has stated that
it will ‘measure the increased performance of the school system as a whole by reference to
international tables of student attainment, such as PISA’ (quoted in Scott 2016). Compare the
confident use of PISA (below), by the then-Secretary of State for Education Michael Gove, and the
more circumspect view offered by an academic critic:
Since the 1990s our performance in these league tables has been at best, stagnant, at worst
declining. In the latest results we are 21st amongst 65 participants in the world for science,
23rd for reading and 26th for mathematics. For all the well-intentioned efforts of past 5
governments we are still falling further behind the best-performing school systems in the
world. (Gove 2013)
‘There are very few things you can summarise with a number and yet Pisa claims to be able to
capture a country’s entire education system in just three of them. It can’t be possible.’ Dr
Hugh Morrison, Queen’s University Belfast (quoted in Stewart 2013).
Numbers and Accountability
On both sides of the Atlantic, policy-makers have argued that statistics will allow greater
‘accountability’ in education. But the thinking behind such claims is flawed in numerous ways. As
Linda Darling-Hammond (2007) has noted, for example, under NCLB the numerous wider structural
inequities that shape educational outcomes are ignored by focusing attention at the school level:
…the wealthiest US public schools spend at least 10 times more than the poorest schools …
Although the Act orders schools to ensure that 100% of students test at levels identified as
‘proficient’ … the small per-pupil dollar allocation the law makes to schools serving lowincome students is well under 10% of schools’ total spending, far too little to correct these
conditions (247-8)
Additionally, the use of quantitative measures as a form of accountability assumes that the measures
are valid, that is, that the recorded data bear some relevance to the issue/s that lie behind the targets.
But there is often scope for cheating and some high-profile cases have emerged. In England, for
example, documented cases include teachers altering students’ work and a school that removed lowattaining students from its official roll in advance of high-stakes testing, thereby artificially raising the
proportion of students deemed ‘successful’ (Harding 2015).In the US, David Hursh notes that gaming
the system can produce considerable rewards:
Rodney Paige, as superintendent of the Houston Independent School District (and later
chosen to be President [GW] Bush’s first Secretary of Education) … [ordered] principals to
not list a student as dropping out but as having left for another school or some reason other
than dropping out. Such creative book-keeping resulted in the district claiming a greatly
reduced dropout rate of 1.5% in 2001–02 and winning a national award for excellence (Hursh
2007, 302)
Numbers and Equity
In the UK, government policy puts numbers at the heart of its proclaimed strategy to create a fairer
society. The Conservative Party, which formed the dominant partner in the Coalition Government 6
(2010-2015), went into the 2010 general election with arguments about ‘transparency’ threaded
throughout their Party Manifesto. This included the promise, emphasized as a bold sub-heading, to
‘Publish data so the public can hold government to account’ (Conservative Party 2010, 69).
Subsequently the rhetoric was translated into a policy that envisaged ‘the public’ using statistics to
understand, challenge and then change the behaviour of public authorities, including the Government
itself:
‘Our proposals,’ the Government Equalities Office (GEO) has said, ‘use the power of
transparency to help public bodies to fulfil the aims of the Equality Duty to eliminate
discrimination, advance equality of opportunity and foster good relations between different
groups. This means that public bodies will be judged by citizens on the basis of clear
information about the equality results they achieve… Public authorities will have flexibility in
deciding what information to publish, and will be held to account by the people they serve.’
(quoted by Instead Consultancy 2011)
This approach embodies a series of assumptions that imbue numbers with an almost magical status
and power. First, it is assumed that relevant and useful data will be made available (despite the
selection being in the gift of the very authorities that ‘the public’ are expected to challenge). Second,
this model of transparent data and active citizenship assumes that the citizenry have the time,
resources and expertise to access the data and then analyse it. Finally, the approach takes for granted
that public bodies will automatically change their behaviour if the data reveal poor ‘equality results’.
Unfortunately, in the real world, none of these assumptions is true.
Statistics do not simply lie around waiting for interested citizens to pick them up and use them.
Numbers are no more obvious, neutral and factual than any other form of data. Statistics are socially
constructed in exactly the same way that interview data and survey returns are constructed, i.e.
through a design process that includes, for example, decisions about which issues should (and should
not) be researched, what kinds of question should be asked, how information is to be analysed, and
which findings should be shared publicly. Even given the very best intentions (and notwithstanding
the opportunity for game-playing and ‘creative book-keeping’ of the sort already documented above)
at every stage there is the possibility for decisions to be taken that obscure or misrepresent issues that
could be vital to those concerned with social justice. In view of the limits of space, a single – but
important – example will suffice. It concerns racial justice and the question of access to, and
achievement in, UK higher education.
It is a scandal that ethnic minority kids are more likely to go to university than poor white
ones 7
The Telegraph (Kirkup 2015)
White British pupils least likely to go to university, says research
The Guardian (Khomami 2015)
White British pupils fall behind ethnic groups in race for university: All minorities now more
likely to go into higher education
Daily Mail (Doughty 2015)
These headlines appeared in the British daily press in November 2015 when an economic think tank
(the Institute for Fiscal Studies - IFS) publicized a review of government figures showing the
proportion of young people going into university from different ethnic groups (Crawford & Greaves
2015). First, as we might expect when applying a CRT perspective that is sensitive to the positioning
of White people at the heart of contemporary politics, it is striking that the relatively low rate for
White students is the angle highlighted by all news outlets regardless of their political positioning.
Including, for example, the most left-wing (Guardian) and right-wing (Telegraph and Mail) parts of
the mainstream British media.
A second important aspect to this story, that may surprise some readers, is that there is nothing new in
the fact that White students are less likely to enter British universities than their peers in most
minoritized groups. This pattern was already known 18 years before these headlines: ‘relative to their
share in the population … ethnic minorities overall are now better represented in HE than whites’
(Coffield & Vignoles 1997 original emphasis).
From the perspective of this paper, focusing on the mis/uses of numbers in race analyses, perhaps the
most important aspect of the IFS report, and the associated newspaper headlines, is that a focus on
access statistics in isolation gives an extremely partial, indeed biased, view of race and Higher
Education in Britain. Simply looking at who goes to university ignores long-standing and significant
race inequities in the status of the universities attended and the level of final degree achievement.
Figure 1 about here
Figure 1 shows the likelihood of attending an elite research-intensive university in the UK (the socalled ‘Russell Group’ of universities).[ 2 ] White and minoritized students appear to have roughly
similar chances of attending elite universities if all minoritized students are lumped together in a
single ‘non-White’ group, usually referred to as BME in the UK (Black and Minority Ethnic).
However, if the minoritized students are disaggregated into smaller and more meaningful groups, 8
some important differences emerge. Figure 2 compares the proportion of White young people entering
Russell Group universities against the rate for the most- and least-likely minority ethnic groups,
Indian and Black Caribbean students respectively.[ 3 ] White British students are almost five times more
likely to gain access to elite research-intensive universities than their peers of Black Caribbean
background. This is a sizeable inequality of opportunity but is invisible in calculations that simply
aggregate all minoritized students (such as Figure 1) or which look at access to all universities
regardless of their standing (such as the national headlines quoted above).
Figure 2 about here
The inflammatory headlines that proclaimed the ‘scandal’ of White rates of access to university
(above) draw attention away from a further facet of race inequity in the system, i.e. differing levels of
achievement between ethnic groups. Table 1 shows the proportions of students in each main ethnic
group attaining the different classes of degree available at the end of their undergraduate studies;
ranging from the very best result (a first class degree) through to a ‘third’ or ‘pass’ degree
classification. White students are more likely to gain a ‘First’ than any other group (22.4%); Black
students are the least likely to be awarded first class degrees (8.7% of Black students overall). This
means that the odds of White undergraduates achieving the highest degree classification are around
three times higher than their Black peers.[4 ] This is a significant ethnic inequality but, perhaps
because the beneficiaries are White, it goes entirely unremarked in the press furore about the overall
access statistics (above).
Table 1 about here
It is clear, therefore, that there is nothing obvious, neutral nor simple about education statistics and
race. In this section, we have reviewed official data that describe differences in university access and
achievement in relation to the ethnic origin of undergraduates in British universities. The government,
an economic think tank and the mainstream media all chose to highlight the apparent underrepresentation of White students (when looking at access across the entire system). This played into
the ongoing high-profile political and media narrative that paints White people as race-victims in
contemporary Britain (see Gillborn 2008 & 2010b; Sveinsson 2009 for critical commentaries). But a
very different picture emerges if the data are questioned in relation to a critical understanding of past
race inequities in education. Such a perspective prompts us to explore differences in the status of
institutions and the levels of achievement at the end of higher education. In both cases, White students
appear to do rather well and, in terms of achievement, better than every other group. Indeed, there is
perhaps scope for further headlines questioning what is happening in British higher education when
the ethnic group that is least likely to go to university nevertheless enjoys the best chance of achieving 9
the top grade. Were this a minoritized group there might be headlines about ‘scandals’ and shocks but,
since the group in question is White, their high attainment fits with the basic expectations of a White
supremacist media and polity and so the pattern goes entirely unremarked.
Big Data: big trouble?
The world’s capacity to store, broadcast and compute information is growing exponentially.
The numbers involved have already passed well beyond the scales we are used to in our
everyday lives. Counting across all forms of storage, from mobile phone memory to DVD,
Blu-Ray and hard disks, we estimate that the world’s installed capacity to store information
will reach around 2.5 zettabytes this year … If we stored all this data on DVDs and piled
them up, the stack of discs would stretch one-and-a-half times the distance from the earth to
the moon. What’s more, this figure is growing by over 50% year-on-year. (Yiu 2012, 10)
‘Big data’ is an increasingly popular phrase used to describe sets of numeric data that are, according
to its advocates, simply too huge for traditional forms of human analysis. Big Data has become big
business. A recent google search for the phrase produced almost 300,000,000 hits[ 5 ] and governments
on both sides of the Atlantic are investing heavily in the technology and talking up its transformative
powers:
Big Data is a Big Deal … Today, the Obama Administration is announcing the “Big Data
Research and Development Initiative.” By improving our ability to extract knowledge and
insights from large and complex collections of digital data, the initiative promises to help
accelerate the pace of discovery in science and engineering, strengthen our national security,
and transform teaching and learning. (WhiteHouse.gov 2012)
It is estimated that the big data market will benefit the UK economy by £216 billion and
create 58,000 new jobs before 2017 … Universities and Science Minister David Willetts
said: “Big data is 1 of the 8 great technologies of the future and a priority for government. It
has the potential to transform public and private sector organisations, drive research and
development, increase productivity and innovation, and enable market-changing products and
services.” (Department for Business, Innovation & Skills 2014 )
Big Data advocates promote a hard sell about the fabulous powers of Big Data. They describe a world
where new possibilities are revealed by an analysis entirely driven by machines and where, most
significantly, theories and human reasoning are rendered obsolete because the ‘numbers speak for 10
themselves’: the following extract is from an article in Wired magazine, entitled ‘The End of Theory’,
which did much to popularize the idea:
This is a world where massive amounts of data and applied mathematics replace every other
tool that might be brought to bear. Out with every theory of human behavior, from linguistics
to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what
they do? The point is they do it, and we can track and measure it with unprecedented fidelity.
With enough data, the numbers speak for themselves. (Anderson 2008)
The argument that numbers can now ‘speak for themselves’ is a popular refrain in Big Data
discussions. Speaking on BBC radio in 2013, for example, author Kenneth Cukier stated:
‘We have to let the data speak for itself. (…) When we trust the data – look at the data – it is a
little bit less biased - in some respects, not in all respects - than we are. And therefore it can
find correlations that we simply, as human beings, can’t because we have limited capacity
(…) the vast amount of data has expanded, we now have to give it to the machine to do what
it does best, and that is parse through it to come up with insights.’[ 6 ]
Cukier’s emphasis on correlations echoes part of Anderson’s argument from Wired:
"Correlation is enough." We can stop looking for models. We can analyze the data without
hypotheses about what it might show. (Anderson 2008)
This is a deliberate and self-conscious rejection of the traditional warning that correlation should not
be mistaken for causation. When Big Data advocates ask us to ‘trust the data’ they paint a picture of
analysis as an almost mystical process that takes place inside machines and is too complex for human
beings to comprehend: ‘We can throw the numbers into the biggest computing clusters the world has
ever seen and let statistical algorithms find patterns where science cannot’ (Anderson 2008). As we
noted at the very start of this paper, however, algorithms are not free from bias: ‘Even algorithms are
biased against black men’ (Naughton 2016; see also Larson et al. 2016). And the reason that
algorithms can be racist is that they are created and interpreted by human beings, many of whom
share commonly held racist stereotypes.
As we have argued above, all data is manufactured and all analysis is driven by human decisions.
Although ‘Big Data’ advocates proclaim its insight and authority with almost evangelical fervour, the
limits of the approach can be found lurking in the small print. For example, in a book whose sub-title 11
proclaims Big Data as a ‘revolution that will transform how we live, work and think’, Cukier and his
co-author accept (contrary to Anderson’s proclamation of the ‘end of theory’) that:
‘… big-data analysis is based on theories, we can’t escape them. They shape both our
methods and our results. It begins with how we select the data. Our decisions may be driven
by convenience: Is the data readily available? Or by economics: Can the data be captured
cheaply? Our choices are influenced by theories. What we choose influences what we find…
(Mayer-Schonberger & Cukier 2013, 72 emphasis added).
This echoes our key argument that all data gathering and analysis is shaped by theories and beliefs
that are susceptible to racial bias. In the next part of the paper we set out some ideas for how the
analysis of quantitative data might usefully be informed by the principles of Critical Race Theory
(CRT).
QuantCrit: TOWARDS A CRITICAL RACE THEORY OF STATISTICS
We [have] defined ‘White logic’ as ‘the epistemological arm of White supremacy’. Rather
than leading to a science of objectivity, White logic has fostered an ethnocentric orientation.
Most researchers have embraced the assumptions of White supremacy. (Zuberi & BonillaSilva 2008, 332)
Critical race-conscious scholars have long questioned the assumptions that shape the accepted
‘mainstream’ definitions of science and rationality. Indeed, ‘challenging claims of neutrality’ and
‘objectivity’ was highlighted as a defining characteristic of CRT in educational studies from the very
start (Ladson-Billings & Tate 1995, 56). In this section of our paper we wish to build upon these
previous studies in order to identify some principles that are explicitly derived from CRT to guide the
interpretation and use of quantitative data.
Critical Race Theory has enjoyed a huge growth in awareness and popularity over the last decade or
so. There is no space (nor need) to recap on the detail of the movement here, suffice it to say that CRT
is now recognized as one of the most important approaches globally for scholars researching, and
opposing, race inequity. CRT has grown rapidly since its early development as an insurgent
movement among US legal scholars of color in the 1970s and 1980s (Bell 1980a & 1980b; Crenshaw
2002; Delgado 1995; Delgado & Stefancic 2001; Matsuda, Lawrence, Delgado & Crenshaw 1993).
CRT has spread into numerous disciplines and now enjoys a global reach, especially in the field of
education (Dixson & Rousseau 2006; Gillborn 2005; Ladson-Billings 1998; Ladson-Billings & Tate 12
1995; Lynn & Dixson 2013; Parker 1998; Solórzano & Yosso 2002; Taylor 2000; Warmington 2012).
One of the most exciting aspects in the growth of CRT has been the development of off-shoot
movements that apply the principles of CRT to the particular experience of one or more monoritized
group, such as Latino CRT (LatCrit)(Montoya & Valdes 2009; Solórzano & Delgado Bernal 2001). A
particularly important recent development has been the move by critical disability scholars to
consciously apply CRT principles in an attempt to generate new insights through a combination of
approaches that they term Disability Critical Race Theory (DisCrit)(Annamma, Connor & Ferri 2013;
Connor, Ferri & Annamma 2016). DisCrit was consciously shaped through the development of a
series of tenets that would provide a starting point for scholars seeking to advance intersectional
research on racism and disability (Annamma et al. 2013, 11). We believe that a similar approach
offers a sound basis for developing key principles to help guide the use of statistics using the insights
of CRT.
Of course, we are not the first to apply CRT to quantitative data and analyses; here we seek to build
on and extend previous approaches. For example, Earnestyne Sullivan and colleagues have used the
term ‘CritQuant’ to describe an approach to quantitative policy analyses that seeks to embody two
‘CRT tenets’, namely the ‘permanence of racism and critique of liberalism’ (Sullivan 2007; Sullivan,
Larke & Webb-Hasan 2010, 77). This is a useful start but we see potential in going beyond just two
tenets and, like the proponents of DisCrit, wish to build a series of sensitizing concepts and principles
that embody a more holistic view of CRT. In order to distinguish our approach, therefore, we have
reversed the elements of Sullivan’s label and directly echo the formulation adopted by Annamma and
colleagues, by adopting ‘QuantCrit’ as a shorthand for our approach.
QuantCrit seeks to extend some of the earlier criticisms of quantitative research on race and
education, made by one of us (Gillborn 2010a), and shares key aspirations with the framework for
‘Critical Race Quantitative Intersectionality’ (CRQI) outlined by Alejandro Covarrubias and Verónica
Vélez (2013). Like them, we seek to generate ‘a framework guided by CRT’ (2013, 275) not a new
theory in its own right. In particular, we wish to emphasize that we do not view this as in any way an
off-shoot movement of CRT; we see the following QuantCrit principles as a kind of toolkit that
embodies the need to apply CRT understandings and insights whenever quantitative data is used in
research and/or encountered in policy and practice. Our approach shares many core assumptions with
Covarrubias & Vélez’s critique including, for example, the view that numbers do not ‘speak for
themselves’ (2013, 278). However, unlike CRQI, we remain fundamentally sceptical about the
possibility that numbers can ever fully capture the ‘material impact’ of intersectional racism or ‘grant
us greater opportunities to effect change at the policy level’ (2013, 282). History suggests that
progress toward race equity occurs when White interests are thought to align with greater social
justice (Bell 1980b; Delgado 2006; Donnor, J. 2016) rather than following from the style and 13
persuasiveness of data that are provided in service of the argument (Covarrubias & Vélez 2013, 271).
This is because, as we have noted above and detail further below, numbers have no objective reality
beyond the frameworks of meaning and politics that create them.
In the rest of this section we outline some first principles for QuantCrit, which can be summarized as
follows:
-
the centrality of racism
-
numbers are not neutral
-
categories are neither ‘natural’ nor given: for ‘race’ read ‘racism’
-
voice and insight: data cannot ‘speak for itself’
-
using numbers for social justice
-
The Centrality of Racism: QuantCrit recognizes that racism is a complex, fluid and changing
characteristic of society that is not automatically nor obviously amenable to statistical inquiry. In the
absence of a critical race-conscious perspective, quantitative analyses will tend to remake and
legitimate existing race inequities.
At the heart of our approach is an understanding that ‘race’ is ‘more than just a variable’ (Dixson &
Lynn 2013, 3). This is more than a methodological statement, it is also a political statement that is
integral to CRT’s model of the social. Social relationships are not readily amenable to quantification;
statistical significance is an arbitrary measure, proving nothing, that is entirely different to social/
historical significance (Ziliak & McCloskey 2008). Of central importance here is the realization that
‘race’ is only ever a social construct - a dynamic of power (history, culture, economics,
representation):
Placing race at the center is less easy than one might expect, for one must do this with due
recognition of its complexity. Race is not a stable category ... ‘It’ is not a thing, a reified object
that can be measured as if it were a simple biological entity. Race is a construction, a set of fully
social relationships.’ (Apple 2001, 204 original emphasis)
It follows that every attempt to ‘measure’ the social in relation to ‘race’ can only offer a crude
approximation that risks fundamentally misunderstanding and misrepresenting the true nature of the
social dynamics that are at play. We noted earlier that quantitative data are frequently assumed to be
more trustworthy and robust than qualitative evidence; but this is turned on its head when we take
seriously the social character of ‘race’. Even the most basic numbers in relation to race equality are
open to multiple and profound threats to their meaning and use. In view of these problems (and the 14
societal dominance of perspectives that are shaped by the interests, perceptions and assumptions of
White people) a sensible starting point in any quantitative analysis is to interrogate the collection,
analysis and representation of statistical material for likely bias in favour of the racial status quo.
- Numbers are not neutral: QuantCrit exposes how quantitative data is often gathered and analyzed
in ways that reflect the interests, assumptions and perceptions of White elites. One of the tasks of
QuantCrit is to challenge the past and current ways in which quantitative research has served White
Supremacy, e.g. by lending support to deficit theories without acknowledging alternative critical and
radical interpretations; by removing racism from discussion by using tools, models and techniques
that fail to take account of racism as a central factor in daily life; and by lending supposedly
‘objective’ support to Eurocentric and White Supremacist ideas.
In the same way that CRT rejects ideologies of neutrality and meritocracy as ‘camouflages’ for racist
interests (Tate 1997, 235), QuantCrit prompts researchers to examine behind the numbers in order to
understand how findings have been generated and identify the racist logics that may have shaped
conclusions. For example, there is a tendency in some quantitative analyses to disguise and even
normalize race inequity. Alice Bradbury (2011) has shown how an expectation of lower achievement
by Black Caribbean students is built into the fabric of quantitative systems by which English schools
are judged. In order to be ‘fair’ to schools, when calculating the amount of progress that their students
made (‘growth’ in US terms), the notion of ‘Contextual Value Added’ (CVA) was developed. This
system calculated the amount of progress that students would usually be expected to make in view of
certain ‘factors’ known to be associated with different rates of attainment, including social
disadvantage and ethnic origin. Schools suffered no penalty if their Black Caribbean students failed to
match the attainment of White British students because the system expected such a pattern and
‘corrected’ for it. As Bradbury notes, ‘whatever the pattern of the coefficients the principle that is
legitimised by CVA is the same: that ethnicity affects how much progress you should be expected to
make’ (2011, 238). This system takes an existing inequity (the lower attainment of previous
generations of Black students) and uses it to ‘predict’ a future where such inequity is normal.
This normalization of lower racialized attainment is not restricted to official analyses; the same kind
of thinking can be found in academic treatments. Stephen Gorard & Emma Smith, for example, have
followed Thorndike (1963, 19) in arguing that ‘under-achievement’ should be defined as
‘achievement falling below what would be forecast from our most informed and accurate prediction,
based on a team of predictor variables’ (2008, 708, emphasis added). In this way, statisticians would
re-define certain levels of achievement inequity as unproblematic; if Black students do as badly as
they are predicted (based on previous cohorts) then they would no longer be ‘under-achieving’. As
Power & Frandji (2010) have noted, these sorts of calculation may sometimes spring from good 15
intentions, e.g. to recognize the relative achievements of traditionally disadvantaged groups, or to
avoid schools being ranked as failures based on raw attainment data that ignores the multiple and
severe challenges facing some communities. Regardless of intent, however, such moves threaten to
enshrine the lower average achievements of some groups as normal, even inevitable:
To some extent, the attempt to valorise the relative successes of disadvantaged schools and
disadvantaged children is to accept their educational inferiority as inevitable and insurmountable
…Rather than insist on the need to level the playing field, we change the definition of success.
And setting different criteria of success for different kinds of pupils inscribes their failure as
‘normal’ and ‘natural’. Through ‘correcting’ schools’ unequal attainments in this way, the new
politics of recognition introduces a disempowering fatalism into the education system. (Power &
Frandji 2010, 393)
These problems amount to the colonisation of interpretation, i.e. by mobilizing statistics in these ways
commentators (including governments and independent academics) act to redefine the facts of
educational achievement and equity. By presenting numbers as a neutral technology (free from
political interference and sentimentality) statisticians sometimes act to assert that their view is the
only true or legitimate understanding of the world, a view where inequitable educational achievement
by some minoritized groups is taken for granted, normalized, and consequently erased from the
agenda.
- Categories/Groups are neither ‘natural’ nor given: for ‘race’ read ‘racism’. QuantCrit
interrogates the nature and consequences of the categories that are used within quantitative research.
In particular, we must always remain sensitive for possibilities of ‘categorical alignment’ (Artiles
2011, Epstein 2007) where complex, historically situated and contested terms (like race and
dis/ability) are normalized and mobilized as labeling, organizing and controlling devices in research
and measurement. Where ‘race’ is associated with an unequal outcome it is likely to indicate the
operation of racism but mainstream interpretations may erroneously impute ‘race’ as a cause in its
own right, as if the minoritized group is inherently deficient somehow.
Even the most basic decisions in research design can have fundamental consequences for the
re/presentation of race inequity. Many studies do not include race/ethnicity as a variable at all; the
absence of race ‘findings’ may then be taken by readers to mean that race/racism is unimportant
whereas it was simply not considered. If ‘race’ is to be included, we have already shown (above)
some of the numerous ways in which the complex and fluid operation of racist labels can come to be
treated as if these social constructs (which change between time and place) represent real ‘things’ –
facts of biology and/or fate. 16
If race and/or ethnicity are to be included in a study then how these ideas are operationalized will
shape the findings. For example, we have noted above, in relation to access to elite British
universities, that White students appear to be disadvantaged when compared with a crude BME
composite group (that lumps together all minoritized students); and yet the same White students
emerge as relatively privileged when compared with their Black Caribbean peers (see Figures 1 & 2).
We have frequently encountered White analysts who proclaim that race was not a factor when, in fact,
they have simply compared White students against everyone else (in a crude non-white composite).
Critical race scholars instantly recognize the meaninglessness of such a binary comparison but trying
to be more sensitive to race complexities is no easy matter. If using too few ethnic categories is one
way to produce meaningless results, then using too many categories can be almost as bad. For
example, we once worked with a school that claimed to conduct rigorous ethnic monitoring and found
no significant differences between ethnic groups’ attainment; on closer inspection we discovered that
the school used a list of more than 70 separate ethnic categories, meaning that few of the cell sizes
contained enough students to have any confidence in the results.
A particular problem in quantitative research on race is that ‘race’ is frequently interpreted as if it
signals a pre-existing fixed quality (or lack of it). In particular, Black groups in the UK and African
American and Latinex students in the US, are often viewed through a deficit lens by politicians,
teachers and academics alike. This means that research which may have been intended to expose and
challenge a race inequity becomes yet more fodder for racist practices and beliefs. Imagine, for
example, that a project finds that ‘race was significantly correlated with lower achievement’. A
critical race theorist will likely interpret the sentence to mean that racism is a significant factor that
affects the chances of achieving. But uncritical White observers, practitioners and policy-makers may
take away the message that some races are less able to achieve. One way of prompting ourselves to
question such thinking is to automatically replace terms like ‘race’ and ‘ethnic origin’ with the couplet
‘race/racism’. The idea of ‘race’ always carries the inherent threat of racist assumptions and actions
(Leonardo 2013; Omi & Winant 1993) and so the move is conceptually legitimate and useful in the
practical sense of prompting the reader to view race critically as a social construct that historically
separates and oppresses particular groups.
Unfortunately, academic research and education policy is replete with examples where race is treated
as having a priori existence that explains inequality by reference to assumed deficits on the part of
minoritized groups. The following example is from the first education policy statement issued by a
newly elected British government in 2010: 17
We must also address serious issues of inequality – both black boys and pupils receiving free
school meals are three times more likely to be excluded than average. Giving teachers the
power to intervene early and firmly to tackle disruptive behaviour can get these children’s
lives back on track. (DfE 2010, para 3.5)
It is sobering that disproportionate expulsion from school is highlighted as a ‘serious’ issue of
‘inequality’ and yet the proposed solution is to give teachers more powers to penalize ‘disruptive
behavior.’ Clearly the government assumed that the exclusion problem lay in the behavior of Black
students and not the racialised disciplinary regimes that historically over-exclude Black students from
British schools (see Blair 2001; Gillborn 2008). As usual, good-intentions are no protection against
slipping into the erroneous belief in race as a fixed identity and a causal factor in its own right. Under
the heading ‘equality areas’, for example, a report seeking to identify inequalities in British higher
education offered the following definition:
Black and minority ethnic
This definition is widely recognised and used to identify patterns of marginalisation and
segregation caused by an individual’s ethnicity. (Equality Challenge Unit 2014, 5).
Racist patterns of inequality (in access, graduation and achievement) are associated with ethnic
origin; a critical scholar would look to identify ways in which racism has shaped these outcomes; but
such ‘patterns’ are in no way ‘caused by an individual’s ethnicity’. Adopting our suggested technique
of using a ‘race/racism’ couplet (above) helps to disrupt such thinking; the sentence would now read:
This definition is widely recognised and used to identify patterns of marginalisation and
segregation caused by race/racism.
- Voice and Insight: data cannot ‘speak for itself’. QuantCrit recognizes that data is open to
numerous (and conflicting) interpretations and, therefore, QuantCrit assigns particular importance to
the experiential knowledge of people of color and other ‘outsider’ groups (including those
marginalized by assumptions around class, gender, sexuality, and dis/ability) and seeks to foreground
their insights, knowledge and understandings to inform research, analyses, and critique.
As we have already noted (see above in relation to Big Data), numbers are social constructs and likely
to embody the dominant (racist) assumptions that shape contemporary society. At every stage in the
production of statistics there is the opportunity for racialized assumptions to come into play.
Consequently, in many cases, numbers speak for White racial interests; their presentation, as objective 18
and factual, merely adds to the danger of racist stereotyping where uncritical taken-for-granted
understandings lay at the heart of analyses.
Quantitative analyses that claim to control for the separate influence of different factors are especially
prone to misunderstanding and misrepresentation. Such ‘regression’ analyses rely on statistical
models that are complex and often only partially explained in published accounts. Nevertheless, the
results are frequently reported as if they describe the real world rather than being an artifact of
statistical manipulations. Regression analyses can turn reality on its head. In an earlier paper, for
example, we described a prominent research study in which several minoritized groups were less
likely to gain access to a higher level of teaching and assessment. However, the researchers performed
a regression analysis that claimed to control for the separate influence of numerous factors (such as
maternal education, socio-economic background and prior attainment); the regression analysis
described most of the minority groups as being over-represented (the reverse of their representation in
the real world) and this was the finding that was reported in the press (Gillborn 2010a, 261-3).
A vital problem lies in the failure of many analysts to realize that racism does not operate separately
to factors such as prior attainment, income, and maternal education. Racism operates through and
between many of these factors simultaneously. In a society that is structured by racial domination, the
impact of racism will be reflected across many different indicators simultaneously. By trying to
disentangle these elements regression analyses imagine that numerous factors (including prior
attainment, socio-economic status and parental education) are entirely independent of racist
influences. Worse still, they treat inequalities in those indicators as if they are a sign of internal deficit
on the part of the minoritized group rather than a socially constituted injustice. The use of ‘prior
attainment’ scores is a particularly important example of this. Quantitative researchers frequently use
students’ test results at an earlier stage of their education as a way to group students of similar
‘ability’, comparing ‘like-with-like’, but this erases racism and blames the students:
the racism that the kids experience on a daily basis [in ranked teaching groups, with restricted
curricula and less-experienced teachers] translates into lower scores … But those scores are
then used to gauge “ability” and “prior attainment” …the differences in prior attainment are
treated as if they were deficits in the students themselves and nothing to do with their schools
(Gillborn 2010a, 266).
- Social justice/equity orientation: QuantCrit rejects false and self-serving notions of statistical
research as value-free and politically neutral. CRT scholarship is oriented to support social justice
goals and work to achieve equity, e.g. by critiquing official analyses that trade on deficit assumptions, 19
and working with minoritized communities and activist groups to provide more insightful, sensitive
and useful research that adds a quantitative dimension to anti-oppressive praxis.
This does not mean that critical race theorists should dispense with quantitative approaches but that
they should adopt a position of principled ambivalence, neither rejecting numbers out of hand nor
falling into the trap of imagining that numeric data have any kind of enhanced status, value, or
neutrality. This is a stance that anti-racist scholars and activists have long practiced, for example,
when they contest supposedly scientific claims about the biological nature of race - sometimes by
invoking what science tells us about the unscientific status of race (Warmington 2009). Critical race
theorists work simultaneously with and against race, i.e. we know that race only exists as a social
construct, but we recognize the sometimes murderous power of the fiction and seek to engage, resist
and ultimately destroy race/racism. Similarly, QuantCrit should work with/against numbers by
engaging with statistics as a fully social aspect of how race/racism is constantly made and legitimated
in society. Like Covarrubias & Vélez (2013, 271) we see hope in the fact that policy-makers
preference for numbers might offer a role for statistics in the radical critique of White supremacy, but
we emphasize that this is a deeply misguided preference which has a habit of evaporating when the
numbers tell an unwelcome story:
Humanism’s search for an originary, or genetic, human experience is quickly betrayed when,
upon deconstruction, human experience appears cultural or racial (usually Eurocentric or
White), and not universal. So what initially appears as general becomes a front for the
universalization of a particular racialized experience. (Leonardo 2005, 405)
CONCLUSION
‘The real danger is not that computers will begin to think like men, but that men will begin to
think like computers.’ Sydney J. Harris (in O’Hagan 2011)
Quantitative data is often used to shut down, silence and belittle equity work. Whenever governments,
employers, or educators, are challenged on their poor performance in relation to an under-represented
group, they will typically reach for statistics in an effort to show that they are really much better than
you might think. Such responses usually involve highly selective decisions about which populations
to include in the calculations, how recently the data were collected, and which other variables might
be used to recalculate the numbers and produce a result more to the liking of the institution that is
under fire. Despite all these numerous decisions and manipulations, many people continue to assume
that numbers have some form of inherent value – more objective, factual and real than ‘mere’ 20
testimony or human experience. Such assumptions are not only incorrect, they are dangerous. In this
paper we have argued that quantitative data are socially constructed in exactly the same way as other
forms of research material (including interviews and ethnographic observations). Numbers’
authoritative façade often hides a series of assumptions and practices which mean, more often than
not, that statistics will embody the dominant assumptions that shape inequity in society. Radical
scholars are right to be suspicious of quantitative material; the data are often generated and analyzed
by people with little interest in, or understanding of, social inequality. Qualitative data, exploring
people’s complex and multifaceted experiences and perspectives, may be inherently better suited to
exposing and opposing racist social processes. However, we believe that there is value in trying to use
statistics responsibly and toward radical egalitarian ends; we have proposed that a useful way ahead
would be to adapt some of the tenets of critical race theory and apply them to the specific issues faced
when handling quantitative data.
We have proposed five principles that might usefully guide early attempts to practice quantitative
critical race theory (or ‘QuantCrit’).
-
the centrality of racism
-
numbers are not neutral
-
categories are neither ‘natural’ nor given: for ‘race’ read ‘racism’
-
voice and insight: data cannot ‘speak for itself’
-
using numbers for social justice
The principles are explicitly modeled on the basic tenets of CRT and we expect that, like CRT itself,
QuantCrit will take on new forms as it is practiced by scholars facing a range of challenges in
different contexts. To date, quantitative data have not featured significantly in CRT scholarship and,
as we have shown, there is good reason for this. Nevertheless, we believe that statistical analyses have
the potential to be used in the service of equity goals, not least to expose and delegitimize the racist
(and sexist, classist, hetero-normative, and ablest) assumptions, policies and practices that are
currently supported by the uncritical use of quantitative data. 21
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.
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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, Brown, Dillabough, & Halsey 2006). Neoliberalism typically assumes that success reflects individual merit and hard work, and that private provision is inherently superior to public. Neoliberalism often works through colour-blind language that dismisses race-conscious criticism as irrelevant, meaningless and/or inflammatory (see Gillborn 2014).
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 (no date).
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/colour 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 2012). 27
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 2007, 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 (2013).