Bayesian Latent Variable Models for Discrete Choice Data

This dissertation consists of three research projects that center on measuring voters’ and political representatives’ multi-dimensional latent preferences based on their binary opinions on public policy issues. This dissertation engages with the broad political methodology literature on pairwise comparison models, Dirichlet process mixture models, and Bayesian IRT models. I innovate statistical models to uncover partisan perceptions of information, to identify voting coalitions, and to estimate multi-dimensional latent preferences. I analyze survey data and roll call vote data, including both existing and newly-collected data. These studies advance the understanding of important political science topics, such as political biases in people’s perceptions of COVID-19 related statements, politicization of human rights in the United Nations, and multiple issue dimensions of legislators’ ideal points.PHDPolitical ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168022/1/yuqiushi_1.pd