因果推理与COVID:使用状态评估评估大流行影响的对比方法

IF 2.7 4区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Educational Measurement-Issues and Practice Pub Date : 2023-02-03 DOI:10.1111/emip.12540
Benjamin R. Shear
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引用次数: 0

摘要

2021年春,也就是学校因COVID-19而被迫关闭仅仅一年后,为了提供有关大流行对学生学习影响的数据,并帮助将资源定向到最需要的地方,政府付出了巨大代价进行了评估。本文使用来自科罗拉多州的州评估数据,描述了使用州评估数据对学生学习进行有效推断以研究大流行影响的最大威胁:影响分数可比性的测量伪影、长期趋势和受测人群的变化。本文比较了三种统计方法(公平趋势、基线学生增长百分位数和人口统计学协变量的多元回归),这些方法可以支持关于大流行期间和受测人群随时间变化的其他情况下学生学习情况的更有效推断。这三种方法对全州学生的表现得出了相似的结论,但对学生分组的推断却截然不同。结果表明,在统计上控制大流行前的人口统计学差异,可以扭转关于受大流行影响最严重群体的结论和有关优先分配资源的决定。©2023国家教育计量委员会。
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Causal Inference and COVID: Contrasting Methods for Evaluating Pandemic Impacts Using State Assessments

In the spring of 2021, just 1 year after schools were forced to close for COVID-19, state assessments were administered at great expense to provide data about impacts of the pandemic on student learning and to help target resources where they were most needed. Using state assessment data from Colorado, this article describes the biggest threats to making valid inferences about student learning to study pandemic impacts using state assessment data: measurement artifacts affecting the comparability of scores, secular trends, and changes in the tested population. The article compares three statistical approaches (the Fair Trend, baseline student growth percentiles, and multiple regression with demographic covariates) that can support more valid inferences about student learning during the pandemic and in other scenarios in which the tested population changes over time. All three approaches lead to similar inferences about statewide student performance but can lead to very different inferences about student subgroups. Results show that controlling statistically for prepandemic demographic differences can reverse the conclusions about groups most affected by the pandemic and decisions about prioritizing resources.

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来源期刊
CiteScore
3.90
自引率
15.00%
发文量
47
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Issue Information Editorial Issue Cover On the Cover: Gendered Trajectories of Digital Literacy Development: Insights from a Longitudinal Cohort Study Digital Module 36: Applying Intersectionality Theory to Educational Measurement
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