New Statistical Method Maps Overlapping Workplace Inequities

Researchers have introduced a statistical technique designed to capture how overlapping social identities shape workplace experiences, applying it to faculty job satisfaction data to test whether it outperforms existing approaches. The method, known as Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, or MAIHDA, was evaluated using survey responses from faculty at a research-intensive university collected through the Collaborative on Academic Careers in Higher Education, a job satisfaction survey project. The work was authored by Eunbi Sim, Youngjin Cho, and Shinhee Jeong.

Calls to diversify research methodology in organizational intersectionality research are growing to better capture intersectional workplace inequities.

In this study, the authors examined satisfaction with institutional leadership across four nested models that incorporated up to nine social identities and positionalities.

The researchers compared MAIHDA with a saturated unregularized fixed-effects approach (FEA) and with ridge-, LASSO-, and elastic-net-regularized interaction models. The comparison used repeated cross-validation.

As model dimensionality and sparsity increased, MAIHDA showed more stable predictive performance than the saturated fixed-effects approach. The regularized approaches performed similarly to MAIHDA.

Repeated half-sample analyses showed that some global and stratum-specific MAIHDA findings were sensitive to sample reduction and composition. The study therefore demonstrates both the potential and the limitations of MAIHDA for organizational intersectionality research.

Under the sparse, high-dimensional conditions examined, the simulations demonstrated advantages of MAIHDA over the saturated fixed-effects approach. Yet the simulations also showed that identifying specific non-additive strata remained challenging.

The article was received on 31 March 2026 and accepted on 15 October 2026, and is published open access under a Creative Commons Attribution license. The authors note that all claims expressed in the article are solely their own and do not necessarily represent those of their affiliated organizations or the publisher.

Subject of Research: A quantitative method, MAIHDA, for examining intersectional workplace inequities using faculty job satisfaction survey data.

Article Title: Introducing the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to quantitatively examine intersectional workplace inequities

Article References: Sim, E., Cho, Y., & Jeong, S. (2026). Introducing the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to quantitatively examine intersectional workplace inequities. Frontiers in Organizational Psychology, 4, Article 1844320. https://doi.org/10.3389/forgp.2026.1844320

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DOI: 10.3389/forgp.2026.1844320

Keywords: MAIHDA, intersectionality, workplace inequities, faculty job satisfaction, multilevel analysis, discriminatory accuracy, organizational research methods, cross-validation, ridge regression, LASSO, elastic net, critical quantitative inquiry