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The Statistical Advantages of Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy for Estimating Intersectional Inequalities

Leckie, George LU ; Bell, Andrew ; Merlo, Juan LU orcid ; Subramanian, SV V. LU and Evans, Clare (2025) In Sociological Methods and Research
Abstract

Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) is a multilevel regression approach grounded in intersectionality theory. It examines inequalities across intersections of social identities (e.g., gender, ethnicity, class) and is argued to provide more accurate predictions of intersectional means than conventional methods that estimate group means directly or via regressions with all interactions. This study evaluates that claim using analytic expressions and an empirical illustration to compare simple and MAIHDA-predicted means against population values. Predictive accuracy is assessed via variance, correlation, bias, and mean squared error. Results show that MAIHDA estimates generally outperform... (More)

Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) is a multilevel regression approach grounded in intersectionality theory. It examines inequalities across intersections of social identities (e.g., gender, ethnicity, class) and is argued to provide more accurate predictions of intersectional means than conventional methods that estimate group means directly or via regressions with all interactions. This study evaluates that claim using analytic expressions and an empirical illustration to compare simple and MAIHDA-predicted means against population values. Predictive accuracy is assessed via variance, correlation, bias, and mean squared error. Results show that MAIHDA estimates generally outperform simple means, particularly when decomposing intersectional means into additive and non-additive identity effects. The magnitude of the advantage depends on inequality patterns and group sample sizes. MAIHDA is especially valuable when inequalities are subtle or data for marginalized intersections are sparse—conditions common in practice. These findings highlight MAIHDA's practical relevance for quantitative intersectionality research.

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; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
empirical Bayes, inequalities, intersectionality, multilevel analysis of individual heterogeneity and discriminatory accuracy, multilevel models, posterior means, predicted means
in
Sociological Methods and Research
article number
00491241251385123
publisher
SAGE Publications
external identifiers
  • scopus:105019608571
ISSN
0049-1241
DOI
10.1177/00491241251385123
language
English
LU publication?
yes
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Publisher Copyright: © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
id
08eafcd9-1c77-4c54-9a01-d7a69b855c8b
date added to LUP
2026-01-16 15:32:50
date last changed
2026-01-17 03:18:44
@article{08eafcd9-1c77-4c54-9a01-d7a69b855c8b,
  abstract     = {{<p>Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) is a multilevel regression approach grounded in intersectionality theory. It examines inequalities across intersections of social identities (e.g., gender, ethnicity, class) and is argued to provide more accurate predictions of intersectional means than conventional methods that estimate group means directly or via regressions with all interactions. This study evaluates that claim using analytic expressions and an empirical illustration to compare simple and MAIHDA-predicted means against population values. Predictive accuracy is assessed via variance, correlation, bias, and mean squared error. Results show that MAIHDA estimates generally outperform simple means, particularly when decomposing intersectional means into additive and non-additive identity effects. The magnitude of the advantage depends on inequality patterns and group sample sizes. MAIHDA is especially valuable when inequalities are subtle or data for marginalized intersections are sparse—conditions common in practice. These findings highlight MAIHDA's practical relevance for quantitative intersectionality research.</p>}},
  author       = {{Leckie, George and Bell, Andrew and Merlo, Juan and Subramanian, SV V. and Evans, Clare}},
  issn         = {{0049-1241}},
  keywords     = {{empirical Bayes; inequalities; intersectionality; multilevel analysis of individual heterogeneity and discriminatory accuracy; multilevel models; posterior means; predicted means}},
  language     = {{eng}},
  publisher    = {{SAGE Publications}},
  series       = {{Sociological Methods and Research}},
  title        = {{The Statistical Advantages of Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy for Estimating Intersectional Inequalities}},
  url          = {{http://dx.doi.org/10.1177/00491241251385123}},
  doi          = {{10.1177/00491241251385123}},
  year         = {{2025}},
}