  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.
