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This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed-effects models when performing intersectional studies. The research questions are as follows: (1) What are typical strata representation rates and outcomes on physics research-based assessments? (2) To what extent do MAIHDA models create more accurate predicted strata outcomes than fixed-effects models? and (3) To what extent do MAIHDA models allow the modeling of smaller strata sample sizes? We simulated 3,000 data sets based on real-world data from 5,955 students on the LASSO platform. We found that MAIHDA created more accurate and precise predictions than fixed-effects models. We also found that using MAIHDA could allow researchers to disaggregate their data further, creating smaller group sample sizes while maintaining more accurate findings than fixed-effects models. We recommend using MAIHDA over fixed-effects models for intersectional investigations.
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Conducting Intersectional Quantitative Analysis with MAIHDA for Education Research
Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) enables intersectional quantitative educational research with distinct advantages over fixed-effects models. Using data from 9,672 physics students across 40 institutions, we compared MAIHDA to traditional fixed-effects models to assess the two methods’ theoretical alignment with intersectionality and ability to model outcomes for diverse social groups. The results indicated that MAIHDA provided more precise measures of outcomes for 95 of the 106 intersectional groups. The manuscript offers guidance for applying MAIHDA in educational research, including R code, and emphasizes the responsibility of researchers to consider critical quantitative theory throughout the research process.
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- Award ID(s):
- 2322015
- PAR ID:
- 10648462
- Publisher / Repository:
- Philosophy Documentation Center
- Date Published:
- Journal Name:
- Inquiry: Critical Thinking Across the Disciplines
- Volume:
- 34
- Issue:
- 2
- ISSN:
- 1093-1082
- Page Range / eLocation ID:
- 387 to 425
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5840/inquiryct202593029
