Date of Award
Doctor of Philosophy (PhD)
One of the most significant assumptions we invoke when making quantitative inferences is the conditional independence between observations. There are, however, many situations when we may doubt this independence. For instance, two seemingly distinct data-generating processes may in fact share unobserved relations. Time-series and cross-sectional studies are also plagued by a lack of independence. If we ignore this common violation of our fundamental modeling assumptions we may draw improper conclusions from our data. This dissertation introduces two methods to the political science literature: a zero-inflated multivariate ordered probit and Gaussian process regression for time-series cross-sectional analyses. This latter model is then applied to demonstrate that executives in Latin America enjoy increased public support following ideological moderation, but executives are less willing to moderate during election years. These effects, however, are conditional on the extremity of the executive. The dissertation as a whole contributes both methodologically and theoretically to the field.
Chair and Committee
Jacob M. Montgomery
Roman Garnett, Jeff Gill, Guillermo Rosas, Margit Tavits,
Carlson, David George, "Advanced Methods in Comparative Politics: Modeling Without Conditional Independence" (2018). Arts & Sciences Electronic Theses and Dissertations. 1517.