Keynote abstracts
Advancing MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy: Towards Contextual Intersectional Multilevel Modelling?
Professor Anne Laure Humbert
University of Gothenburg
Quantitative research has often struggled to engage meaningfully with intersectionality. Traditional methods, such as cross-tabulations or models with interaction terms, can rarely capture the structural, relational, and contextual dimensions of intersectional inequalities. The MAIHDA approach (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) has offered a promising alternative, particularly by simplifying complex model specifications and highlighting variation between intersectional groups. However, its application to date has largely remained within the paradigm of ‘descriptive intersectionality’.
This talk presents how MAIHDA can be extended to support ‘analytical intersectionality’, an approach concerned not only with documenting intersectional inequalities, but also with analysing the processes and structures that produce and sustain them. Drawing on data from the Joint European and World Values Surveys, I demonstrate how intersectional multilevel models can be used to explore variation across identity groups and national contexts. I argue for a shift in how the MAIHDA approach can be used: from modelling individual differences to interrogating structural inequalities. This extended methodological framework, what might be called (contextual) intersectional multilevel modelling or (C)I-MLM, opens up new possibilities for critical, theoretically informed, and methodologically rigorous research on intersectional inequalities in the economy and society.
Biography
Professor Anne Laure Humbert, PhD, is a researcher at the University of Gothenburg, Sweden. Anne is very experienced in gender equality research at national, EU and international level, policy analysis and assessment as well as gender statistics. She specialises in applying quantitative methods to comparative social and economic analysis. Anne is a regular public speaker on gender equality and intersectionality, and she enjoys the opportunity to make connections between theory, policy and practice. Her recent methodological work has focused on the applications of intersectional multilevel modelling (MAIHDA: Multilevel analysis of individual heterogeneity and discriminatory accuracy) to various aspects of social and economic outcomes of inequalities, and she is currently writing a book on the topic based on her experience of training other researchers on how to use this new approach to intersectional quantitative analysis.
The Statistical Advantages of MAIHDA for Estimating Intersectional Inequalities
Professor George Leckie
University of Bristol
Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) is a multilevel regression modeling approach, rooted in intersectionality theory, for examining social inequalities across intersections of multiple social identities (e.g., gender, ethnicity, social class). Proponents argue that MAIHDA’s predicted intersectional means are statistically superior to simple means from descriptive statistics or conventional regression models, but this claim remains largely untested. We derive and analyze analytical expressions to compare the variance and correlation of predicted intersection means from two MAIHDA-based predictors and simple mean calculations with the distribution of true but unknown population means. Additionally, we assess the bias, variance, and mean squared error when predicting the mean of a given intersection. Our findings show that MAIHDA-based means outperform simple means, particularly when using the predictor that decomposes intersectional means into additive and non-additive components of social identities. However, the relative advantage of each MAIHDA predictor depends on the nature of intersectional inequalities and intersection sizes. MAIHDA’s benefits are most pronounced when inequalities are subtle or when data on certain intersections, such as those for marginalized groups, are sparse—conditions common in practice, highlighting the practical significance of our findings.
Biography
George is a Professor of Social Statistics and Co-Director of the Centre for Multilevel Modelling (CMM) at the University of Bristol, UK. His research interests focus on the application and dissemination of multilevel modelling. He has applied this method to study the effects of institutional and geographical contexts on sociodemographic inequalities in individual educational and health outcomes. A long-standing interest of his has been examining school effects on student learning through school value-added models, as well as engaging in debates regarding the use of such data to inform school accountability and choice. A more recent interest has been the application of multilevel modelling, specifically MAIHDA, to study intersectional inequalities.
(Im)possible categorisation? Exploring the challenges and contributions of intersectional quantitative research from a mixed-race perspective
Dr Rhianna Garrett
Loughborough University
The UK has become a global hub of multiculturalism, containing an intricate tapestry of what it means to hold a racialised identity. Yet, mixed-race identities continue to be limited to colonial ‘race’ categories that do not reflect the identities of mixed-race populations in Britain today. This presentation highlights the key challenges involved in conducting intersectional quantitative research from a mixed-race perspective, while also articulating the benefits of incorporating multiracial considerations into quantitative intersectional research. To achieve this, the discussion will present key findings from a new collaborative paper that employs Multiple Correspondence Analysis (MCA) on the EVANS survey to uncover new empirical insights into mixed-race Britain. Dr Garrett aims to encourage scholars to work outside of their methodological comfort zones and continue to make multiracial matters matter.
Biography
Dr Rhianna Garrett is a multiple award-winning mixed methods researcher, consultant and community organiser at Loughborough University’s Department of Geography and Environment. Her work explores intersectional mixed-race identities in Britain, exposing how white supremacist monoracial norms shape society—an issue increasingly urgent amid the rise of far-right politics. As Global Coordinator for the Critical Mixed Race Studies Association’s (CMRSA) executive board, she also examines how ideas of race, ethnicity, and culture shift across different geopolitical contexts. Inspired by liberatory decolonial and feminist approaches, Rhianna draws on both creative qualitative methods and critical quantitative methods to centre lived experiences and reveal monoracism’s widespread influence on British conceptualisations of ‘race’.
How Doing Intersectional Quantitative Research questions statistical convention
Professor Niels Spierings
Radboud University
A broadly shared position is that intersectionality and doing quantitative research are not mutually exclusive. Frameworks have been developed on what kind of intersectionality fits quantitative research. Debate is on-going on what is minimally needed for quantitative research to be considered intersectional. And the umber of manuals and ‘how to’-guides is growing. Building on these developments in the field of doing intersectional quantitative research (DIQR), in this keynote I want think out loud on how DIQR raises issues about statistical conventions. Particularly, I aim to discuss how certain conventions and practices might be particularly problematic for DIQR, and what this means for the role (quantitative intersectionalist) academics have in the process of academic research.
Biography
Niels Spierings is full professor in sociology, processes of inclusion and exclusion, at Radboud University (Nijmegen, the Netherlands). His work focuses mainly on the position of ethno-religious minorities and LGBTQ+ people, including exlcusionary populist politics, in Western Europe. As part of his research, he likes to think and write about combining feminist, critical and intersectional insights into quantitative research. For instance, he wrote the chapter ‘Quantitative Intersectional Research: Approaches, Practices, and Needs’ in The Routledge international handbook of intersectionality studies.