Using linked education administrative data to evaluate and extend traditional studies of school effects on student outcomes

使用关联的教育管理数据来评估和扩展学校对学生成绩影响的传统研究

• 批准号: 2882727
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882727
• 关键词: Using; linked; education; administrative; data

CONTEXTSchool effectiveness researchers estimate individual school effects as a first step to identifying school policies and practices which might explain why some schools are more effective than others. The DfE estimates individual school effects to hold schools to account (Progress 8), to inform school inspections (Ofsted), to inform parents choosing schools (compare-school-performance.service.gov.uk), and to provide schools with information for self-improvement.APPROACHThe standard approach is to use the NPD to fit school 'value-added' fixed or random effect linear regression models to student GCSE examination scores where student KS2 test scores and sociodemographics (age, gender, ethnicity, language, FSM, SEN, IDACI) are entered as covariates to attempt to adjust the resulting predicted school effects for school differences in the student body at intake which would otherwise bias the school comparisons.TOPIC 1We will use the Census 2011 student, household, and neighbourhood characteristics available in the Wave 1 GUIE data (2014/15 cohort) to extend the standard approach to make far richer adjustments for school differences in the student body at intake. We will study the importance of these additional adjustments and their impact on the predicted school effects and in doing so we will assess the adequacy or not of the standard approach to address the non-random assignment of students to schools.TOPIC 2We will use students' employment and earnings 1, 3, 5 and 10 years after graduation available in LEO to extend the standard approach to explore school differences on students' longer-term labour market outcomes. Specifically, we will change the dependent variable in the standard approach to different labour market outcomes and we will explore models with and without GCSE score entered as a covariate. We will study the resulting predicted school effects and how they change across the different outcomes to highlight the inequalities in the school system.

背景学校效率研究人员估计个别学校的影响是确定学校政策和实践的第一步，这可能解释为什么有些学校比其他学校更有效。教育部长评估个别学校的影响，以使学校承担责任（Progress 8），通知学校检查（Ofsted），通知选择学校的家长（compare-school-performance.service.gov.uk），并为学校提供自我完善的信息。标准方法是使用NPD将学校的“增值”固定效应或随机效应线性回归模型拟合到学生GCSE考试成绩中，其中学生KS2考试成绩和社会人口统计学（年龄、性别、种族、语言、FSM、SEN、IDACI）作为协变量输入，试图调整由此产生的预测学校效应，以适应入学时学生群体的学校差异，否则会使学校比较产生偏差。主题1我们将使用第1波GUIE数据（2014/15队列）中提供的2011年人口普查学生、家庭和社区特征来扩展标准方法，以便在入学时对学生群体的学校差异进行更丰富的调整。我们会研究这些额外调整的重要性，以及它们对学校预期影响的影响。在研究过程中，我们会评估标准方法是否足以解决学生被随机分配到学校的问题。主题2：我们将使用LEO提供的学生毕业后1、3、5和10年的就业和收入来扩展标准方法，以探索学校对学生长期劳动力市场结果的差异。具体来说，我们将改变标准方法中的因变量，以适应不同的劳动力市场结果，我们将探索有和没有GCSE分数作为协变量输入的模型。我们将研究结果预测的学校影响，以及它们如何在不同的结果中变化，以突出学校系统中的不平等。