AI 中文总结
本文针对两时期被试内设计构建潜在结果框架，分析合并估计量与延续效应检验，推导敏感性分析，支持被试内设计并建议证明延续效应差值为零及报告其偏离的敏感性。
AI 中文摘要
近期研究鼓励政治科学家从仅采用后测设计转向被试内设计，以通过重复测量提升精度。我们针对两时期被试内设计构建了潜在结果框架，该框架允许不等分配及异质性处理效应与延续效应，刻画了合并估计量并评估了用于证明合并合理性的延续效应检验。研究发现：其一，仅当两种处理序列的平均延续效应差值为零时，合并才能识别平均处理效应；合并估计量的单位聚类标准误与其基于设计的对应标准误一致。其二，在温和条件下，延续效应检验的效力显著低于仅用后测数据的平均处理效应检验；仅在检验未被拒绝时才进行合并的两步程序会产生通常覆盖不足的置信区间，当差值为零时，覆盖不足仅发生在合并比后测分析更高效（即被试内设计更具价值）的情况；当差值非零时，除非差值或样本量较大，否则覆盖不足较为常见。其三，我们推导了敏感性分析，发现已发表的结论对合理的延续效应差值具有稳健性。因此，我们支持被试内设计，但建议从实质上证明延续效应差值为零，并报告其偏离的敏感性。
英文摘要
Recent work encourages political scientists to move from post-only toward within-subject designs for improved precision from repeated measurements. We formalize a potential-outcomes framework for two-period within-subject designs that allows for unequal allocation and heterogeneous treatment and carryover effects. We characterize the pooled estimator and evaluate the carryover test used to justify pooling. We find: first, pooling identifies the average treatment effect only when the gap in the average carryover effects is zero across the two treatment sequences. The unit-clustered standard error for the pooled estimator is identical to its design-based counterpart. Second, under mild conditions, the carryover test has strictly less power than the average-treatment-effect test with post-only data. The resulting two-step procedure, which pools only after a nonrejected test, produces confidence intervals that typically undercover. When the gap is zero, undercoverage occurs if and only if pooling is more efficient than post-only analysis, precisely when the within-subject design is worthwhile. When the gap is nonzero, undercoverage is typical unless the gap or sample size is large. Third, we derive a sensitivity analysis and find published conclusions robust to plausible carryover gaps. We therefore endorse within-subject designs but recommend justifying a zero carryover gap substantively and reporting sensitivity to departures.
