学生成绩中的社会人口不平等:对个体异质性和判别准确性的交叉多层次分析(MAIHDA)

IF 1.8 2区 社会学 Q2 ETHNIC STUDIES Sociology of Race and Ethnicity Pub Date : 2024-08-03 DOI:10.1177/23326492241267251
Lucy Prior, Clare Evans, Juan Merlo, George Leckie
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引用次数: 0

摘要

学生成绩方面的社会人口不平等是教育系统一直关注的问题,而且人们越来越认识到这种不平等是交叉性的。交叉性考虑了不利条件的多维性,理解了影响个人经历的相互交织的社会决定因素。个体异质性和歧视准确性的交叉多层次分析(MAIHDA)是人口健康领域开发的一种新方法,但在教育研究领域尚属首次。在本研究中,我们引入并应用了这一方法来研究英国伦敦两批学生的社会人口学成就不平等现象。我们根据学生的年龄、性别、免费学校膳食状况、特殊教育需求和种族组合,定义了 144 个交叉层。我们发现,各层次的成绩差异很大,主要是由叠加效应而非互动效应造成的,而且两批学生的结果始终保持一致。我们的结论是,政策制定者应更多地关注多重边缘化学生,交叉性 MAIHDA 为研究他们的经历提供了一种有用的方法。
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Sociodemographic Inequalities in Student Achievement: An Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)
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 new to 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 stratum-level variation in achievement composed primarily by additive rather than interactive effects with results stubbornly consistent across the two 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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CiteScore
4.90
自引率
6.70%
发文量
62
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