Decolonising quantitative research methods pedagogy: Teaching contemporary politics to challenge hierarchies from data

AuthorNadine Zwiener-Collins,Juvaria Jafri,Rima Saini,Tabitha Poulter
DOIhttp://doi.org/10.1177/02633957211041449
Published date01 February 2023
Date01 February 2023
Subject MatterLearning and Teaching in Politics and International Studies
https://doi.org/10.1177/02633957211041449
Politics
2023, Vol. 43(1) 122 –138
© The Author(s) 2021
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DOI: 10.1177/02633957211041449
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Decolonising quantitative
research methods pedagogy:
Teaching contemporary
politics to challenge
hierarchies from data
Nadine Zwiener-Collins
University of Salzburg, Austria
Juvaria Jafri
University of Cambridge, UK
Rima Saini
Middlesex University, UK
Tabitha Poulter
City, University of London, UK
Abstract
Decolonisation of the curriculum in higher education is a radical, transformative process of change
that interrogates the enduring Eurocentric and racist narratives surrounding the production of
academic ‘knowledge’. Our key argument is that it is essential for students of politics to understand
the authorities and hierarchies exerted through quantitative data. In this article, we show that
(1) quantitative methods and data literacy can be an explicit tool in the endeavour to challenge
structures of oppression, and (2) there is a need to apply decolonial principles to the teaching of
quantitative methods, prioritising the historical contextualisation and anti-racist critique of the
ways in which statistics amplify existing micro and macro power relations. To explain how this
can be done, we begin with a commentary on the ‘state of decolonisation’ in higher education, its
relevance to the subdisciplines of politics, and its application to quantitative teaching in the United
Kingdom. We then suggest some guiding principles for a decolonial approach to quantitative
methods teaching and present substantive examples from political sociology, international political
economy, and international development. These suggestions and examples show how a decolonial
lens advances critical and emancipatory thinking in undergraduate students of politics when it is
used with quantitative methods.
Corresponding author:
Nadine Zwiener-Collins, Department of Political Science, University of Salzburg, Sigmund-Haffner-Gasse 18,
Salzburg 5020, Austria.
Email: nadine.zwiener-collins@sbg.ac.at
1041449POL0010.1177/02633957211041449PoliticsZwiener-Collins et al.
research-article2021
Learning and Teaching in Politics
and International Studies
Zwiener-Collins et al. 123
Keywords
decolonisation, diversity and inclusion, pedagogy, quantitative methods, teaching and learning
Received: 17th July 2020; Revised version received: 3rd June 2021; Accepted: 9th July 2021
Introduction
The ability to understand and interpret data is an essential feature of life in the 21st century: vital
for the economy, for our society and for us as individuals . . . Our vision is of a fully data-literate
population, able to engage with data and the world in which we live actively and intelligently’.
(British Academy, 2015: 3)
Whilst many may not call societies ‘backward’ explicitly today, many theories and practices of
development depend on this very assumption. Is this justified? What else does this assume? One
interpretation of ‘decolonising the curriculum’ means interrogating such assumptions, models
and frameworks for these specific biases. (Sabaratnam, 2017)
We argue that the teaching of quantitative methods is an essential component of anti-
hegemonic practice. As such, we build on previous endeavours in which quantitative
research has been notably used to advance racial justice. For instance, it was through
innovative data visualisation and dissemination methodologies that scholars such as
W.E.B. Du Bois challenged negative stereotypes with empirical truth. His novel approach,
based on numerical tools, offers a blueprint for making decolonial studies a theme of the
social sciences and not just the humanities (Battle-Baptiste and Rusert, 2018). We take
inspiration from this nexus, noting that quantitative data are a tool to critique authorities
and hierarchies, and pivotal to a decolonial approach for teaching politics.
As such, our article explores how two ostensibly separate initiatives – the project to
mainstream quantitative methods teaching and the endeavour to decolonise higher
education – can be combined to generate a pedagogical strategy that is effective and
opportune for contemporary politics curricula. Our approach is informed by several
years of experience teaching quantitative methods to diverse cohorts of undergraduate
social science students at various UK higher education institutions. We argue that
quantitative methods are an avenue for ‘mainstreaming’ the decolonisation discourse;
at the same time, quantitative methods teaching needs to be decolonised. For this, we
propose a deeper engagement – in the classroom – with the nature of data, particularly
where they come from and what they do.
Data gathering1 is a form of knowledge production. Undergraduate students must be
equipped to grapple with the authoritarian, hierarchical, and hegemonic nature of data.
Data literacy is thus an explicit tool to critique mainstream social and political discourses
around social justice. Drawing on our teaching practice, we centre on two learning out-
comes: (1) to understand the power relations that underlie knowledge production, and (2)
to describe and critically analyse social data, in secondary as well as primary forms, while
acknowledging their biases. We suggest that a decolonial approach to teaching methods
should emerge from the following four activities: (1) resisting Eurocentrism in our own
teaching, (2) showing how politics and quantitative methods are systematically
Eurocentric, (3) integrating race critical thinking in quantitative methods teaching, and
(4) encouraging students to create and use data critically.
Our focus is on curricula, but this is not a pedagogical article; instead, we take a hybrid
approach to discuss decolonisation and quantitative methods teaching in a theoretical and

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