Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects
Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects
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DOI：
10.1198/016214508000000841
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发表时间：
2008-12-01
影响因子：
3.7
通讯作者：
Anderson, Michael L.
中科院分区：
文献类型：
--
作者：
Anderson, Michael L.
有观点认为，早期教育的投资回报最高。儿童干预被广泛接受，主要源于几个有影响力的随机试验——abecedarian， Perry。以及早期培训项目——这些都表明早期干预会带来超正常的回报。本文对这些实验进行了重新分析，重点关注两个在以前的分析中受到有限关注的核心问题：治疗效果的性别异质性和由于多重推断而导致的零假设的过度拒绝。解决后一个问题。采用一种统计框架，将摘要索引测试与家庭错误率和错误发现率校正结合起来，减少了测试的次数，后两种技术调整了多重推理的p值。重新分析的主要发现是，女孩从干预中获得了大量的短期和长期益处，但男孩没有显著的长期益处。这些结论。当使用无法调整多重测试的“幼稚”估计器时，它们显得模棱两可。为越来越多的关于男女学业成绩差距的文献做出贡献。他们还证明，在复杂的研究中，对相同的数据集提出多个问题，声明正在考虑的测试族，合并测量或报告调整和未调整的p值是很重要的。
The view that the returns to educational investments are highest for early. childhood interventions is widely held and steins primarily from several influential randomized trials-Abecedarian, Perry. and the Early Training Project-that point to super-normal returns to early interventions. This article presents it de novo analysis of these experiments, focusing on two core issues that have received limited attention in previous analyses: treatment effect heterogeneity by gender and overrejection of the null hypothesis due to multiple inference. To address the latter issue. a statistical framework thin combines summary index tests with familywise error rate and false discovery rate corrections is implemented The first technique reduces the number of tests conducted: the latter two techniques adjust the p values for multiple inference. The primary finding of the reanalysis is that girls garnered substantial short- and long-term benefits from the interventions, but there were no significant long-term benefits for boys. These conclusions. which have appeared ambiguous when using "naive" estimators that fail to adjust for multiple testing. contribute to a growing literature on the emerging female-male academic achievement gap. They also demonstrate that in complex studies where Multiple questions are asked of the same data set, it can be important to declare the family of tests under consideration and to either consolidate measures or report adjusted and unadjusted p values.