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What does the MAIHDA method explain?

Rima Wilkes1, Aryan Karimi1

  • 1Sociology, 6303 NW Marine Drive, UBC, Canada.

Social Science & Medicine (1982)
|February 24, 2024
PubMed
Summary

Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) mis-specifies intersectional models. The study demonstrates that strata function as individual-level variables, not contexts, leading to model redundancy and inaccuracy.

Keywords:
Health disparitiesIntersectionalityMAIHDAMultilevel modellingQuantitative methods

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Area of Science:

  • Quantitative intersectional modeling
  • Statistical methodology
  • Health disparities research

Background:

  • Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a novel quantitative intersectional modeling approach.
  • MAIHDA utilizes demographic variables and their combined constructs (strata) to analyze outcomes.
  • The method aims to uniquely identify additive and intersectional effects.

Purpose of the Study:

  • To critically evaluate the methodological claims and operationalization of MAIHDA.
  • To demonstrate the mis-specification of MAIHDA in quantitative intersectional analyses.
  • To propose alternative, more accurate modeling strategies.

Main Methods:

  • Analysis of the MAIHDA framework, specifically its treatment of strata as a level 2 context.
  • Comparison of MAIHDA's multilevel specification with single-level models.
  • Identification of collinearity and redundancy issues arising from variable duplication across model levels.

Main Results:

  • MAIHDA incorrectly operationalizes strata as a level 2 context; strata are individual-level composite variables.
  • The inclusion of demographic variables at both level 1 and level 2 leads to mis-specified, collinear, and redundant models.
  • MAIHDA fails to uniquely identify additive and intersectional effects as promised.

Conclusions:

  • MAIHDA is methodologically flawed for quantitative intersectional analysis.
  • Single-level models incorporating demographic variables with interactions or strata as fixed effects are more accurate.
  • The findings necessitate a re-evaluation of multilevel approaches for intersectional research.