Qualitative research has been on the forefront to demonstrate how the simultaneous interactions of multiple identities shape complex social inequalities (Crenshaw 1989, 1991). Person-centred, narrative accounts of stigma, marginalization, or discrimination have been pivotal to establish that individual experiences of oppression and privilege have an underlying structural dimension that cuts across social categories such as gender, race, ethnicity, sexual orientation, religion, age, socio-economic background, disability.
The preference for qualitative methods by feminist and intersectional scholars is further strengthened by an uneasy relationship with quantitative data. Specifically from an intersectional perspective, quantitative methods create an “ethical conundrum” (Bowleg and Bauer 2016), as their supposed superior capability to provide evidence (e.g. through random sampling) often render oppressed or marginalized communities and their experiences invisible (e.g. small N). To this one might add the most widely used analytical quantitative techniques – e.g. regression analysis with interaction effects – is rather limited when targeting the central point of intersectionality, namely the overlapping, non-reducible forms of exclusion.
Historically, feminist thinkers have also critically engaged with the epistemological claims to objective, universal knowledge often attached to quantitative data and positivism. Qualitative accounts showed that bias exists in terms of what gets measured. What more, disciplinary silos between public health, education, sociology, gender studies and other branches of the social sciences have undermined further the exchange of promising quantitative methods and data for intersectional analysis.
Without engaging in unfruitful discussion about the value of qualitative versus quantitative data and analysis, the present seminar series aims to present promising approaches to the intersectional analysis of quantitative data. A better understanding of contemporary possibilities of an intersectional analysis of quantitative data that is capable of “matrix thinking”, i.e. discern complex patterns of inclusion and exclusion, is crucial to misconceive intersectionality simply as multiple demographic factors, descriptive device, or “diversity” tool (May 2005, x).
The methods and analytical approaches presented in this seminar series, such as for example multilevel analysis, are not necessarily new; in some cases, these are well established techniques whose potential to address the complex, non-additive nature of intersectional social inequality has simply not been sufficiently recognized (Bauer et al. 2021; Else-Quest and Hyde 2016; Haynes et al. 2020). As these techniques usually do not form part of the standard (regression-based) toolbox of the social sciences their usage might be unfamiliar to many, especially those working in the field of gender studies and/or an intersectional perspective. The series of seminars and lectures “Intersectional Social Justice” address these issues by offering an example-based introduction to an intersectional analysis of quantitative data with selected invited speakers and experts in the field.