The number of studies addressing issues of inequality in educational outcomes using cognitive achievement tests and variables from large-scale assessment data has increased. Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables. Italian primary school data from INVALSI large-scale assessments were analyzed using both quantile and standard regression approaches. Mathematics and reading scores were regressed on students' characteristics and geographical variables selected for their theoretical and policy relevance. The results demonstrated that, in Italy, the role of gender and immigrant status varied across the entire conditional distribution of students’ performance. Analogous results emerged pertaining to the difference in students’ performance across Italian geographic areas. These findings suggest that quantile regression analysis is a useful tool to explore the determinants and mechanisms of inequality in educational outcomes. A proper interpretation of quantile estimates may enable teachers to identify effective learning activities and help policymakers to develop tailored programs that increase equity in education.

中文翻译：

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超出平均估计值：使用INVALSI调查数据对教育成果中的不平等现象进行分位数回归分析

使用认知成绩测验和大规模评估数据中的变量来解决教育成果不平等问题的研究数量有所增加。在这里，将使用分位数回归方法的价值与经典回归分析方法进行比较，以研究教育成果与可能的预测变量之间的关系。使用分位数和标准回归方法对来自INVALSI大规模评估的意大利小学数据进行了分析。根据学生的特征和地理变量对数学和阅读分数进行回归，以根据他们的理论和政策相关性进行选择。结果表明，在意大利，性别和移民身份的作用在学生表现的整个条件分布中各不相同。在整个意大利地理区域，学生表现的差异也出现了类似的结果。这些发现表明，分位数回归分析是探索教育成果中不平等的决定因素和机制的有用工具。对分位数估计的正确解释可以使教师识别有效的学习活动，并帮助决策者制定量身定制的计划，以提高教育公平性。