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.

翻译：学生成就中的社会人口学不平等一直是教育系统持续关注的议题，且日益被视为具有交叉性特征。交叉性理论关注弱势群体的多维本质，强调塑造个体经验的交错社会决定因素。交叉性多水平个体异质性与歧视准确性分析（MAIHDA）是人口健康领域发展出的新方法，但尚未广泛应用于教育研究。本研究引入并应用该方法，分析英格兰伦敦两届学生群体在学业成就方面的社会人口学不平等现象。我们根据学生年龄、性别、免费校餐资格、特殊教育需求及种族等变量的组合，定义了144个交叉性分层。研究发现，各分层在成就上存在显著变异，其主要由加性效应而非交互效应构成，且结果在两届群体中表现出高度一致性。我们由此得出结论：政策制定者应更多关注多重边缘化学生群体，而交叉性MAIHDA为研究其经历提供了有效方法。