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Missing, presumed different: Quantifying the risk of attrition bias in education evaluations

机译:缺失,假设不同:量化教育评估中的消耗偏见风险

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We estimate the magnitude of attrition bias for 10 randomized controlled trials (RCTs) in education. We make use of a unique feature of administrative school data in England that allows us to analyse post-test academic outcomes for nearly all students, including those who originally dropped out of the RCTs we analyse. We find that the typical magnitude of attrition bias is 0.015 effect size units (ES), with no estimate greater than 0.034 ES. This suggests that, in practice, the risk of attrition bias is limited. However, this risk should not be ignored as we find some evidence against the common 'Missing At Random' assumption. Attrition appears to be more problematic for treated units. We recommend that researchers incorporate uncertainty due to attrition bias, as well as performing sensitivity analyses based on the types of attrition mechanisms that are observed in practice.
机译:我们估计教育中的10个随机对照试验(RCT)的磨损偏差幅度。 我们利用英格兰的行政学校数据的独特特征,使我们能够为几乎所有学生分析测试后学术结果,包括最初从我们分析的RCT中掉出的人。 我们发现典型的磨损偏差幅度为0.015次效果尺寸单位,估计没有大于0.034 es。 这表明,在实践中,消耗偏差的风险是有限的。 然而,这种风险不应该被忽视,因为我们发现一些针对常见的“随机”假设缺失的证据。 磨损似乎对治疗单位更有问题。 我们建议研究人员由于磨损偏差而掺入不确定性,以及根据实践中观察到的磨损机制类型进行敏感性分析。

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