论文标题
学生成就的社会人口统计学不平等：对个人异质性和歧视准确性（MAIHDA）的交叉多级分析，并向英格兰伦敦的学生应用
Sociodemographic inequalities in student achievement: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) with application to students in London, England
论文作者
论文摘要
学生成就的社会人口统计学不平等是教育系统的持续关注，并且越来越多地被认为是交叉的。交叉性考虑了劣势的多维性质，并欣赏了互锁的社会决定因素，这些决定因素塑造了个人经验。个人异质性和歧视准确性（MAIHDA）的交叉多层分析是一种在人口健康中开发的新方法，但在教育研究中的应用有限。在这项研究中，我们介绍并应用了这种方法来研究英格兰伦敦两名学生的学生成就社会人口统计学不平等。我们定义了144个交叉层，这是由学生年龄，性别，免费学校用餐，特殊教育需求和种族组合的组合产生的。我们发现，成就的实质层级变化主要由添加剂而非交互作用组成，结果在整个队列中固执地保持一致。我们得出的结论是，决策者应更加关注多重边缘化的学生，而Maihda交叉提供了一种有用的方法来研究他们的经验。
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.
