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A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

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  • Evans, Clare
  • Leckie, George
  • Subramanian, SV
  • Bell, Andrew

    (University of Sheffield)

  • Merlo, Juan

Abstract

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses. Please cite this paper as: Evans, C.R., G. Leckie, S.V. Subramanian, A. Bell, & J. Merlo. (2024.). A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). SSM - Population Health. https://doi.org/10.1016/j.ssmph.2024.101664

Suggested Citation

  • Evans, Clare & Leckie, George & Subramanian, SV & Bell, Andrew & Merlo, Juan, 2024. "A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," SocArXiv 635hx, Center for Open Science.
    • Handle: RePEc:osf:socarx:635hx
    DOI: 10.31219/osf.io/635hx
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    References listed on IDEAS

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    1. Evans, Clare R. & Erickson, Natasha, 2019. "Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995–2008," Social Science & Medicine, Elsevier, vol. 220(C), pages 1-11.
    2. Bauer, Greta R. & Scheim, Ayden I., 2019. "Methods for analytic intercategorical intersectionality in quantitative research: Discrimination as a mediator of health inequalities," Social Science & Medicine, Elsevier, vol. 226(C), pages 236-245.
    3. Wemrell, Maria & Mulinari, Shai & Merlo, Juan, 2017. "Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches," Social Science & Medicine, Elsevier, vol. 177(C), pages 213-222.
    4. Aránzazu Hernández-Yumar & Maria Wemrell & Ignacio Abásolo Alessón & Beatriz González López-Valcárcel & George Leckie & Juan Merlo, 2018. "Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-23, December.
    5. Leckie, George & Charlton, Chris, 2013. "runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i11).
    6. Evans, Clare R. & Williams, David R. & Onnela, Jukka-Pekka & Subramanian, S.V., 2018. "A multilevel approach to modeling health inequalities at the intersection of multiple social identities," Social Science & Medicine, Elsevier, vol. 203(C), pages 64-73.
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