We present an aggregation scheme that increases power in randomized controlled trials and quasi-experiments when the intervention possesses a robust and well-articulated theory of change. Longitudinal data analyzing interventions often include multiple observations on individuals, some of which may be more likely to manifest a treatment effect than others. An intervention's theory of change provides guidance as to which of those observations are best situated to exhibit that treatment effect. Our power-maximizing weighting for repeated-measurements with delayed-effects scheme, PWRD aggregation, converts the theory of change into a test statistic with improved asymptotic relative efficiency, delivering tests with greater statistical power. We illustrate this method on an IES-funded cluster randomized trial testing the efficacy of a reading intervention designed to assist early elementary students at risk of falling behind their peers. The salient theory of change holds program benefits to be delayed and non-uniform, experienced after a student's performance stalls. In this instance, the PWRD technique's effect on power is found to be comparable to that of doubling the number of clusters in the experiment.
翻译:我们提出了一个集成计划,在干预措施具有强有力和清楚阐述的变革理论时,增强随机控制试验和准实验能力。纵向数据分析干预往往包括对个人的多重观察,其中有些可能比另一些人更可能表现出治疗效果。干预的理论为这些观察中哪一种最适合展示治疗效果提供了指导。我们用延迟效应计划反复测量的权势最大化权重,PWRD聚合,将变革理论转换成测试统计,提高无症状相对效率,以更大的统计力量进行测试。我们用由IES供资的集束随机试验来说明这种方法,测试旨在帮助有可能落后于同龄早期小学生的阅读干预的功效。明显的变革理论认为,方案效益要推迟,而不是统一,这是在学生表现摊后经历的。在这方面,PWRD技术对权力的影响与实验组数翻倍相似。