In the advent of big data and machine learning, researchers now have a wealth of congressional candidate ideal point estimates at their disposal for theory testing. Weak relationships raise questions about the extent to which they capture a shared quantity -- rather than idiosyncratic, domain-specific factors -- yet different measures are used interchangeably in most substantive analyses. Moreover, questions central to the study of American politics implicate relationships between candidate ideal points and other variables derived from the same data sources, introducing endogeneity. We propose a method, consensus multidimensional scaling (CoMDS), which better aligns with how applied scholars use ideal points in practice. CoMDS captures the shared, stable associations of a set of underlying ideal point estimates and can be interpreted as their common spatial representation. We illustrate the utility of our approach for assessing relationships within domains of existing measures and provide a suite of diagnostic tools to aid in practical usage.

翻译：随着大数据和机器学习时代的到来，研究者如今拥有大量国会候选人理想点估计值可用于理论检验。然而，这些估计值之间较弱的关联性引发了质疑：它们在多大程度上捕捉到了共享的潜在特质，而非特定领域或测量方法独有的因素？尽管如此，在大多数实质性分析中，不同测量方法仍被互换使用。此外，美国政治研究中的核心问题往往涉及候选人理想点与源自同一数据源的其他变量之间的关系，这引入了内生性问题。我们提出了一种新方法——共识多维标度法（CoMDS），该方法更贴合应用学者在实践中使用理想点的方式。CoMDS能够捕捉一组底层理想点估计值之间共享的、稳定的关联，并可被解释为这些估计值的共同空间表征。我们通过实例展示了该方法在评估现有测量方法内部关联性方面的效用，并提供了一套诊断工具以辅助实际应用。