Notes on: Sablan, J (2019) can You Really Measure
That? Combining Critical Race Theory and
Quantitative Methods. American Educational
Research Journal. 56 (1): 178 – 203
DOI: 10.3102/0002831218798325
Dave Harris
[Very informative. I could only follow the basics
of the statistical modelling. I learned about
non-dominant cultural capital and welcomed the
stuff on how people of colour resist racism with
culturl capital of their own. I am sure working
class people do -- I have suggested one bit of
this is know-how on 'poaching']
The argument is sustained by using a survey of
undergraduates to quantify their community
cultural wealth (CCW) using a CRT framework.
We can use quantitative data to look at public
consumption of educational policy research,
including problems of access and equity to
students of colour. However CRT training usually
thinks of stats and quantitative reasoning as
'related to white supremacist origins', so that
strong statistical analysis is usually seen as
'entirely antithetical' (178). Nevertheless there
have been a range of quantitative studies to
examine racial disparity, ranging from 'teacher-student
racial mismatch to student discipline disparities
and college affordability'. [and lots of soft
quantification]. CRT students might doubt whether
statistical and causal inferences can possibly get
at the complexities of structural racism, however.
Nevertheless CRT can be an appropriate framework
for quantitative studies, and quants have been an
important part of critical legal studies in the
past. It needs to reach its full potential. We
might demonstrate this by looking at 'community
cultural wealth' (CCW) a concept often cited
in CRT that emphasises students' assets.
Reviewing CRT first, it's clear that the roots lie
in critical legal studies, noticing that attention
to race was missing, and legal scholarship,
including its intersection with gender
discrimination law. White supremacy and structural
racism was ignored in legal process, and CRT used
legal analysis to show how the implementation of
the law subordinated racial groups, say in various
legal cases where apparent wins for desegregation
actually revealed an alignment between white
interests and black goals all along [presumably
some sort of reformist result that maintained
white supremacy -- see examples here].
Generally, 'legal and civil rights progress is
intimately tied to the establishment of white
supremacy' (179). The same sort of argument can be
extended to studies in education [reference to
Ladson-Billings and Tate 1995).
In education, CRT was adopted to emphasise the
centrality of race, racism and white supremacy in
educational structures and social practice.
Previous theories of gender and class were
insufficient. There should be a theoretically
developed focus, for example to explain why
students of colour were seen as deficient in their
achievements. Instead, the structures in education
and the 'assets communities of colour bring to
school even when faced with oppression' should be
recognised.
Basic tenets should be adhered to: race, racism
and intersections are endemic; dominant frameworks
and ideologies that are white supremacist should
be challenged; scholarship works toward social
Justice and empowerment and 'elimination of racism
and poverty' (180); experiential knowledge of
people of colour is a legitimate way of
understanding, such as through storytelling; CRT
is inter- or transdisciplinary. We therefore get
both the theory and methodology
We can define theory as 'representation of
knowledge based on the systematised framework of
concepts' so CRT implies that the tenets above are
principles to guide enquiry. But there is also
methodology which centres the needs and
experiences of people of colour as a tool to
collect and analyse data. This methodology is
theoretically grounded. It challenges traditional
research paradigms and offers liberatory
frameworks. It focuses on the experiences of
students of colour. It takes interdisciplinary
perspectives. However, it is 'not explicitly
qualitative' although there is a preponderance of
qualitative research, clearly rooted in the role
of stories and counter stories, 'parables,
chronicles, poetry, fiction and revisionist
histories' [legacy of romanticiesed indigenous
stuff?] which may also help shift away from
positivist emphases. White centred or majoritarian
research diminishes the voices and stories of
people of colour so a counterstory deliberately
challenges these master narratives.
Social science research on the whole is not seen
as fully equipped to reflect oppressed
communities, including indigenous and colonised
populations. Hence the perceived incompatibility
between CRT and quants, and the tendency to favour
qualitative methods. One dubious assumption about
quants is that it is bias free, for example which
seems to contradict the need to take a definitive
stance. Quants tend to use positivistic paradigms,
and qualitative methods might be better suited for
critical paradigms and alternative epistemologies
— but this is 'simplistic and limiting because it
equates methods with paradigms which can and
should be distinct… Critiquing the limits of
post-positivism does not negate the potential use
of QMs' (181).
Counter stories and quantitative methods tend to
focus on individuals, while quants emphasise
groups and summary statistics in an attempt to
test models and hypotheses and this might
oversimplify the relations between variables, and
underestimate the complexity of race relations.
But equating counterstory and qualitative enquiry
is also too limited — the response to majoritarian
narratives is 'an equally important function of
the counterstory' and response can be
'accomplished through quantitative studies' (182)
A critical intent might be crucial in the choice
of methods. Quants and statistics have an
unfortunate historical origin in the methods used
to justify the biological inferiority of black
people and in eugenics, but 'this history does not
beget contemporary methods' and 'qualitative
methods are not immune to critique grounded in
perspectives of racism and colonialism such as the
use of ethnography in stereotyping indigenous and
native communities' (182).
Causal inference might be a problem, in
attributing causes to socially constructed notions
like race, and the process of isolating variables
apparently free from selection bias. For CRT, race
is clearly a nonrandom variable and racism is
endemic. However 'there may be some value to
studies that unearth the context of how policies
and practices differentially affect groups'.
Strong teaching and graduate school preparation
should include high quality use of quantitative
methods across educational fields including in CRT
research, for example in graduate school
mentorship. They might inform greater theorising
on race, when encountering explanations other than
race or racism for differences in educational
attainment and experiences, for example. Improving
understanding of both quants and CRT would be an
appropriate response rather than dismissing one
approach.
Issues regarding objectivity, historical origins
and statistical interpretations do limit the use
of quants in critical race research, but many
researchers are also calling for expanded mixed
and quantitative methodologies in CRT, and concern
regarding the lack of attention to race and racism
in education policy literature. Empirical work is
an important driver of CRT development and so
'ensuring the full range of methodological options
is key' (183).. Some researchers have used quants
and this use should be improved
[Several useful-looking references are given for
researchers who have used quants, including the
very appealing Milkman, K. L., Akinola, M.,
& Chugh, D. (2015). What happens before: A
field experiment exploring how pay and
representation differentially shape bias on the
pathway into organizations. Journal of Applied
Psychology, 100, 1678–1712]
A 'quantitative criticalist' uses quants to reveal
inequities, especially systemic ones, and to
question models and analytic practices, a line
that 'originated in the German Frankfurt school'
(183). CRT draws more from civil rights movement
and makes more explicit connections to race and
its intersections, although it also features
realism as a central part and sees social science
research as the foundation for making arguments
about race-based discrimination. It has used
quants to complement qualitative counter
storytelling [simple counts mostly — soft
quantification?]
Combining the two, we can identify a critical
approach to quants is one that emphasises the
assets of students of colour and looks at the
overarching structure of racism and racial
inequity. The specific method has been self
identified as;'QuantCrit'. It has explored
micro-aggressions, racial battle fatigue, and has
analysed achievement and demographic data, for
example by disaggregating Asian and native
Hawaiian Pacific island populations by ethnicity,
social class and immigration status. There has
been argument for '"quantitative
intersectionality"' for example looking at how
Chicana/o students progress through the
educational pipeline and disaggregating the data
by gender, class and citizenship status. [Sounds a
bit like Sewell --
another political impulse for UK CRT enthusiasts
to reject quants of course]. There is a full
acceptance that numbers do not speak for
themselves, that quantitative analysis is grounded
in experiential knowledge and standpoints, and
that research is designed to advance social
justice, and that transdisciplinary approaches are
necessary.
The latter points to parallel studies such as
stratification economics, and social psychological
concepts of racial bias. Stratification economics
broadens human capital deficiencies by looking at
differences in structural resources; psychological
literature has also influenced work on
microaggression and on racial battle fatigue,
structural equation modelling helps researchers
understand how exposure to racial
microaggression contributes to stress in
students of colour.
Quants in CRT studies in education still leaves
much to be desired. There is no definite
incompatibility, but there is still a need for
development, and more methodological guidance to
move into actual research practice. For example
'not all areas of quantitative methodology are
fully used in CRT scholarship' (185). We have lots
of descriptive and demographic statistics, but we
should adopt more culturally responsive methods.
Descriptive statistics need to push on to
establish underlying causes or motivations. This
should produce more experimental designs, like
those that have documented racial discrimination
in professors' perceptions of prospective graduate
students. There is concern about causal modelling,
but we might still proceed with 'predictive,
experimental/quasiexperimental and evaluative
modelling'. Generally we might move beyond
descriptive statistics.
Measurement theory might be explored more and
connected more with CRT tenets and theories,
pursuing perhaps the work on psychological models
of racism and stress. So predictive regression
models, causal inference via quasiexperimental
studies and 'exploratory and confirmatory theory
building (e.g. exploratory factor analysis…
Structural equation modelling)' might be fully
developed.
There may be doubts about the measurability of
some of these complex subjects, and psychometrics
again has dubious origins, but 'when taken with an
appropriate lens' measurement theory, even survey
methodology can adequately contribute. For example
'counterstories can be incorporated into scale
development and validation techniques can refine
asset based theories' (186) we might use
self-report surveys [and develop the idea of
community cultural wealth — CCW]
CCW has become a specific approach within CRT, and
we might well use quants and measurements here. It
is well grounded in CRT frameworks, and these are
well cited 'yet [offer?] empirically
underdeveloped form of quantitative methods'
(187). It refers to the assets students of colour
bring to schooling: 'aspirational capital: the
ability to maintain hopes and dreams for the
future; familial capital: connections to and
knowledge of family and kinship networks;
navigational capital: the ability to navigate
through schooling institutions that were not
designed with communities of colour in mind;
resistant capital: the knowledge of and motivation
to transform oppressive structures'
[Thank God someone has picked up on these
capacities to resist!]
This counters 'dominant notions of cultural
capital as in Bourdieu!. That tends to be deficit
minded! There is a concept of 'non-dominant
cultural capital, the assets that students bring
from their home communities that can guide
academic achievement. The issue also points to
broader issues 'such as racial capitalism', where
schools actually are able to engage students CCW,
even exploit it. [Again another reference looks
interesting Carter, P. L. (2005). Keepin’ it
real: School success beyond Black and White. New
York, NY: Oxford University Press]
We have to be careful when quantifying cultural
capital and we can develop this when quantifying
CCW to avoid reductionism. The intention is to
focus on aspirational, familial, navigational and
resistant capital. These forms have an exchange
value but how schools value them and how students
use them is 'up for future empirical study' (188)
They can be operationalised in four separate
scales, following Yosso, T. J. (2005). Whose
culture has capital? A critical race theory
discussion of community cultural wealth. Race
Ethnicity and Education, 8, 69–91.
doi:10.1080/1361332052000341006.
Sablan did an online survey of undergrads into
open access institutions in the US Pacific
recruiting 'Asian American Native American Pacific
Islanders' -- AANAPISIs. The majority were Pacific
Island or Asian-American, followed by Filipino,
Chamorro, Micronesian and then other Asian or
Pacific islander. 1.4% were white and 19%
multi-ethnic. The majority (75%) were female. Over
60% of students were first entrance to college,
the majority were from low family incomes,
apparently typical of most students of this kind,
typically neglected in educational research. A
series of scales of non-dominant cultural capital
were developed and used in research.
The scales were developed on aspirational capital,
familial, navigational and resistant. Each scale
had 7 to 8 items which asked students to agree on
a six-point scale with how a statement described
them. There was content validity testing, expert
review, pilot testing and cognitive interviewing.
A review of the literature was pursued to address
content validity. Expert reviewers also reviewed
the scales and then cultural community leaders. A
pilot survey was completed by a small group of
similar students at a different institution, and
the results were used to test the dependability of
the design as well as student fatigue — all agreed
that the survey questions were understandable and
of an appropriate length, and other feedback was
incorporated. Cognitive interviews involve
responders from a similar population being asked
questions related to their interpretation of the
survey questions, a kind of '"think aloud"' (190)
where respondents take the survey and then respond
to questions about the questions, explaining what
they think it means and how they got to the
answer, ways in which the questions might be
improved, interpretations of questions that might
contradict the content intended by the researcher,
to see how the items reflected the theoretical
components that were supposed to be
operationalised. Unclear questions were reworded
or deleted. {Examples pp 189]
Classical test theory assumes that the observed
score is composed of the true score plus error,
latent construct refers to traits that are by
their nature unobservable and cannot be measured.
We can use these to see how responses to a set of
items generates a score that approximates to what
is being sought — non-dominant cultural capital.
We can also use factor validity, using factor
analysis, to see how underlying factors predict
the variance in the scale items [actual equation
used on (191) -- the basis of it seems to be
isolating the common factors that underlie the
item, then the factors unique to each item, then
the loading of each item on the factor and the
random measurement error of each item — we can
then examine factor loadings as a measure of how
related the items are to each other, and pick out
those above a particular value. This business of
rejecting items that did not reach an acceptable
value applied to all the tests discussed here].
There are still limitations — she/they only
validated the scale with one study and it would
have been better to try on several, especially
diverse populations, nevertheless, she/they is
satisfied that the sampling procedure is effective
enough, even though more work is needed.
Operationalising CCW in a quantitative approach is
definitely 'a worthy endeavour' overall and it can
substantially improve on previous studies.
The scale showed 'preliminary reliability and
validity evidence' [coefficients are shown lying
between zero and one and all of them scored above
0.7 which is the normal threshold for reliability
measures]. Further statistical analysis was used
to extract factors that best represented the
variance of the item and to examine the
potentially correlated nature of the multiple
factors. Again everything fell within recommended
thresholds, meaning that the process 'could result
in internally consistent scales' although 'further
validation work' would be ideal for future
studies.
Navigational capital and familial capital seem to
be satisfactory items in measuring underlying
factors. Statistically they both seemed to load
onto one common factor which explained around 50%
of the variance with acceptable structure
coefficients. [Each separate scale seemed to be
pretty high-scoring]. Aspirational capital and
resistant capital might need more modification to
'best capture a measurement of these [underlying]
factors' (194), and some items did not
sufficiently load onto a factor, and were deleted
from the scale — aspirations from or related to
the family, the aspiration to surpass parents'
educational and occupational success. The main
factors for resistant capital seem to be
identification of oppression in society and
motivation to transform oppressive structures,
which happens to be consistent with how it is
described in the CRT literature.
Overall, measurement theory can help analyse the
CRT discussion of CCW. It is possible to score
satisfactory factors and then use them in
multivariate models. This shows that an empirical
model of CRT and quants can be useful, and
additional multivariate analyses similarly might
be conducted in terms of college access and CCW.
It certainly seems that aspirational capital,
familial capital, navigational capital and
resistant capital 'have preliminary reliability
and validity evidence', but there may be need to
improve the empirical fit especially with
resistant capital and its alignment with CRT, but
at least we now have guidelines about how to
design and implement projects.
So qualitative methods are not the only way to
present counter narratives. When it comes to asset
based critical race theories, they do seem to be
combinable with quants especially measurement
theory. There are implications for policy and
practice, and there are implications also for the
advancement of social justice. Conventional
quantitative studies might be less useful if
cultural assets are not operationalised.
Institutionalised racism is often absent from
theoretical frameworks, but critical quants should
go on to engage the full potential instead of just
relying on trying to establish non-neutrality.
There are possible ways for quants and CRT to be
congruent and compatible. There is much potential.
We can move beyond a lot of CRT quantitative
studies that are descriptive in nature. We should
be able to develop reliability and validity
techniques of measurement theory to produce more
'theoretically and methodologically driven
studies' (197) we can then go on to discuss more
robust quantitative techniques.
The case study here turning on CCW illustrates the
tensions of this dialogue between quants and CRT.
The work is theoretically robust and
experientially rich, but there are still tensions.
Do the quantitative analyses oversimplify the
complexity by making it a matter of statistical
variables? We still need to discuss how to measure
and analyse CCW, and how it might be an
appropriate framework to understand college access
and readiness. We might need to think about
further measures of it. This should include
quantitative analysis and different scales in
different populations and intersection identities.
What is it that makes quants critical, and how do
we put quantitative analysis into critical
practice. Actually running a regression model or
whatever looks similar whether critical or
non-critical studies are being pursued, and
usually conventional practices 'have long been
interpreted in ways that run counter to CRT
tenets' (198). Perhaps the intent matters more
than the methods?, CRT notes that cultural assets
are not valued by the dominant school culture
rather than just describing the picture [or using
the findings to support a deficit theory?].
We need more methodological guidelines, which
implies conventional teaching, although there may
be methodological practices beyond what is
conventionally taught that will be useful to
issues of race and racism. Unfortunately, limits
have been introduced by the effort to criticise
'the nonneutrality or even racialised dangers of
numbers'. Instead, we need much more
'methodological doing and teaching while also
highlighting the important practice implications'
especially from 'studies that are critically
informed and intended'. [same for reserch methods
generally I reckon]
There are implications for institutions designed
with students' cultures in mind. There is a
criticism of deficit interpretations which often
inform policy, and caution about existing
psychometric tests. Well-designed quantitative
work might challenge these policies. We 'should
move beyond simple descriptive statistics of
racial difference to inferential measurement and
theoretical modelling'(198), if only to provide
the context for counter stories. We will be able
to test theories and assumptions for example those
that emphasise students cultural assets.
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