Educational Inequalities at the Intersection of Multiple Social Categories: An Introduction and Systematic Review of the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) Approach

Abstract

Intersectional approaches have become increasingly important for explaining educational inequalities because they help to improve our understanding of how individual experiences are shaped by simultaneous membership in multiple social categories that are associated with interconnected systems of power, privilege, and oppression. For years, there has been a call in psychological and educational research for quantitative approaches that can account for the intersection of multiple social categories. The present paper introduces the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach, a novel intersectional approach from epidemiology, to study educational inequalities. The MAIHDA approach uses a multilevel model as the statistical framework to define intersectional strata that represent individuals’ membership in multiple social categories. By partitioning the variance within and between intersectional strata, the MAIHDA approach allows identifying intersectional effects at the strata level as well as obtaining information on the discriminatory accuracy of these strata for predicting individual educational outcomes. Compared to conventional quantitative intersectional approaches, MAIHDA analyses have several advantages, including better scalability for higher dimensions, model parsimony, and precision-weighted estimates of strata with small sample sizes. We provide a systematic review of its past application and illustrate its use by analyzing inequalities in reading achievement across 40 unique intersectional strata (combining the social categories of gender, immigrant background, parental education, and parental occupational status) using data from 15-year-old students in Germany (N = 5451). We conclude that the MAIHDA approach is a valuable intersectional tool to study inequalities in educational contexts.

Intersectional Prevalence of Suicide Ideation, Plan, and Attempt Based on Gender, Sexual Orientation, Race and Ethnicity, and Rurality

“…Clustering by social strata operationalizes the assumption that people within the strata are exposed to a common set of social structures (eg, sexism, racism) that might be associated with differing degrees of STB risk and/or resilience . MAIHDA allows for comparison of outcome prevalence for every social stratum (ie, for every combination of advantaged or disadvantaged identities) and has been described as “an analytical standard in epidemiology” for quantitative intersectionality research . To our knowledge, the current study is the first to use MAIHDA to characterize intersectional STB prevalence.…”

Intersectional analysis of social disparities in type 2 diabetes risk among adults in Germany: results from a nationwide population-based survey

“…The calculated variance components measure suggested that 14.00% of the individual variance in diabetes risk score could be attributed to between-strata variance. According to Axelsson Fisk et al [47], this VPC can be considered as indicating a good level of discriminatory accuracy of intersectional strata, and is rather high compared to other studies applying MAIHDA to health outcomes [52]. Thus, a relevant proportion of the variance in diabetes risk scores can be explained at strata level, highlighting the role of social determinants in the risk of developing type 2 diabetes.…”

Mapping the Future of Intersectional Research in Human Resource Development: An Integrated Literature Review

“…While extant IR in HRD has often relied on non-empirical or qualitative data within conceptual and/or intra-categorical approaches, quantitative research methods can also be effective in conducting IR when applied with an intersectional lens (Bauer et al, 2021; Bowleg & Bauer, 2016; Else-Quest & Hyde, 2016; Evans et al, 2018; Fehrenbacher & Patel, 2020). Conventional regression with interactions or intersectional variables is frequently used in quantitative analysis, but it has its limitations, such as the dependency of interaction effects on main effects and issues related to scalability, reductionism in social identities, and model parsimony (Bowleg & Bauer, 2016; Hancock, 2019; Keller et al, 2023). Intersectionality scholars have advocated for more advanced methods, such as person-centered analyses (e.g., latent class analysis), which identify latent intersectional sub-groups based on the similarities in the response patterns (Bauer et al, 2022) and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), which treats social identities as social strata and allows for a more nuanced examination of intersectional effects (Evans et al, 2018).…”