Paired cluster-randomized experiments (pCRTs) are common in education program impact evaluation trials. Although common, there is surprisingly no clear consensus regarding how to analyze this randomization design to estimate average treatment effects. Variance estimation is also complicated due to the dependency created through pairing clusters. Therefore, we aim to provide an intuitive and practical comparison between different estimation strategies for pCRTs to inform practitioners' choice of strategy. To this end, we present a general framework for design-based estimation of an average individual effect in pCRTs. This framework offers a novel and intuitive view on the bias-variance trade-off between point estimators and emphasizes the benefits of covariate adjustment for estimation with pCRTs. In addition to providing a general framework for estimation with pCRTs, the point and variance estimators we present support fixed-sample unbiased estimation with similar precision to a common regression model and conservative variance estimation. Through simulation studies based on an educational efficacy trial, we compare the performance of the point and variance estimators reviewed. Our analysis and simulation studies inform the choice of point and variance estimators for analyzing pCRTs in practice.

翻译：配对整群随机实验(pCRT)广泛应用于教育项目效果评估试验中。尽管这类实验设计很常见，但令人惊讶的是，对于如何分析此类随机化设计以估计平均处理效应，学界尚未形成明确共识。由于配对簇所产生的依赖性，方差估计也变得复杂。因此，我们旨在对pCRT的不同估计策略进行直观且实用的比较，以指导实践者选择策略。为此，我们提出了一个基于设计的通用框架，用于估计pCRT中的平均个体效应。该框架为点估计的偏差-方差权衡提供了新颖直观的视角，并强调了协变量调整对pCRT估计的益处。除了为pCRT估计提供通用框架外，我们提出的点估计量和方差估计量支持固定样本无偏估计，其精度与常见回归模型相当，并能实现保守的方差估计。基于一项教育效能试验的模拟研究，我们比较了所回顾的点估计量和方差估计量的性能。我们的分析与模拟研究为实践中分析pCRT时点估计量和方差估计量的选择提供了参考依据。