学生学业成就的社会人口学不平等：个体异质性与判别准确性的交叉多层次分析（MAIHDA）及其在英格兰伦敦学生中的应用
Sociodemographic inequalities in student achievement: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) with application to students in London, England
AI总结：
本研究将人口健康领域的交叉多层次分析方法MAIHDA引入教育研究，通过分析伦敦两个学生队列的144个交叉阶层，发现学业成就的阶层差异主要源于加性效应且队列间结果一致，为研究多重边缘化学生经历提供了新工具。
摘要：
学生学业成就的社会人口学不平等是教育系统长期存在的问题，且其交叉性日益受到认可。交叉性理论关注劣势的多维度本质，重视塑造个体经历的相互交织的社会决定因素。个体异质性与判别准确性的交叉多层次分析（MAIHDA）是在人口健康领域发展出的新方法，但在教育研究中应用有限。本研究引入并应用该方法，研究英格兰伦敦两个学生队列的学业成就社会人口学不平等。我们基于学生年龄、性别、免费校餐资格、特殊教育需求和族裔的组合，定义了144个交叉阶层。研究发现，学业成就的阶层层面差异显著，主要由加性效应而非交互效应构成，且结果在两个队列间高度一致。我们得出结论：政策制定者应更加关注多重边缘化学生，而交叉MAIHDA为研究其经历提供了有用的方法。
英文摘要：
Sociodemographic inequalities in student achievement are a persistent concern for education systems and are increasingly recognized to be intersectional. Intersectionality considers the multidimensional nature of disadvantage, appreciating the interlocking social determinants which shape individual experience. Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a new approach developed in population health but with limited application in educational research. In this study, we introduce and apply this approach to study sociodemographic inequalities in student achievement across two cohorts of students in London, England. We define 144 intersectional strata arising from combinations of student age, gender, free school meal status, special educational needs, and ethnicity. We find substantial strata-level variation in achievement composed primarily by additive rather than interactive effects with results stubbornly consistent across the cohorts. We conclude that policymakers should pay greater attention to multiply marginalized students and intersectional MAIHDA provides a useful approach to study their experiences.
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