How should researchers analyze randomized experiments in which the main outcome is latent and measured in multiple ways but each measure contains some degree of error? We first identify a critical study-specific noncomparability problem in existing methods for handling multiple measurements, which often rely on strong modeling assumptions or arbitrary standardization. Such approaches render the resulting estimands noncomparable across studies. To address the problem, we describe design-based approaches that enable researchers to identify causal parameters of interest, suggest ways that experimental designs can be augmented so as to make assumptions more credible, and discuss empirical tests of key assumptions. We show that when experimental researchers invest appropriately in multiple outcome measures, an optimally weighted scaled index of these measures enables researchers to obtain efficient and interpretable estimates of causal parameters by applying standard regression. An empirical application illustrates the gains in precision and robustness that multiple outcome measures can provide.

翻译：研究者应如何分析主要结果为潜在变量且通过多种方式测量、但每种测量方式均存在一定程度误差的随机实验？我们首先指出，现有处理多重测量的方法存在一个关键的研究特异性不可比问题，这些方法通常依赖于强建模假设或任意标准化。此类方法导致所得估计量在不同研究之间缺乏可比性。为解决该问题，我们阐述了基于设计的方法，使研究者能够识别感兴趣的因果参数；提出通过增强实验设计以提高假设可信度的方案；并讨论关键假设的实证检验。我们证明，当实验研究者适当投入于多重结果测量时，这些测量指标的最优加权缩放指数能够使研究者通过应用标准回归方法，获得高效且可解释的因果参数估计。一项实证应用展示了多重结果测量在提升精确性与稳健性方面所能带来的增益。