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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接分析随机干预实验的处理效应与性别异质性,因此归入因果推断。

反馈分类问题

Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects

早期干预效果的多重推断与性别差异:三项实验的重新评估

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代码、数据与补充材料

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关注这篇论文的作者

早期干预效果的多重推断与性别差异:三项实验的重新评估

研究问题
早期教育随机实验的收益是否存在性别差异,既有结论又在多大程度上受到同时检验多个结果所产生的过度拒绝影响?
方法与思路
作者重新分析三项早期干预实验,结合汇总指数检验、家族错误率校正与错误发现率校正,考察性别异质性。
材料陈述的贡献
摘要报告女孩获得显著的短期和长期收益,男孩长期收益未达显著;并强调明确检验族及报告多重检验调整结果。
阅读关注点
应核对实验分配与样本处理、检验族和汇总指数的定义、性别差异的直接检验,以及调整前后的效应与不确定性。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要;男孩长期收益不显著不等于效应为零,摘要未说明效应量、检验功效及刊本变化。

原文摘要

The view that the returns to educational investments are highest for early childhood interventions is widely held and stems 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 a 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 that 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.

笔记与分组