Latent factor models are widely used in the social and behavioral science as scaling tools to map discrete multivariate outcomes into low dimensional, continuous scales. In political science, dynamic versions of classical factor models have been widely used to study the evolution of justices' preferences in multi-judge courts. In this paper, we discuss a new dynamic factor model that relies on a latent circular space that can accommodate voting behaviors in which justices commonly understood to be on opposite ends of the ideological spectrum vote together on a substantial number of otherwise closely-divided opinions. We apply this model to data on non-unanimous decisions made by the U.S. Supreme Court between 1937 and 2021, and show that, for most of this period, voting patterns can be better described by a circular latent space.

翻译：潜在因子模型作为缩放工具广泛应用于社会科学与行为科学领域，将离散多变量结果映射至低维连续量表。在政治学中，经典因子模型的动态版本已被广泛用于研究多法官法院中司法立场的演变。本文提出一种基于圆形潜在空间的新型动态因子模型，该模型能容纳如下投票行为：通常被认为处于意识形态光谱对立端的法官，在一系列原本分歧显著的判决中共同投票。我们将该模型应用于1937年至2021年间美国最高法院非一致裁决的数据，结果表明，在此期间大部分时段，投票模式可由圆形潜在空间更贴切地描述。