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Kevin McAlister (University of Michigan)
Roll Call Scaling in the U.S. Congress: Addressing the Deficiencies
Selection committee: Xun Pang (Tsinghua, chair), Arthur Spirling (NYU), and Yiqing Xu (UCSD)
Citation:
The William Prize Committee is delighted to select Kevin McAlister's dissertation proposal "Roll Call Scaling in the U.S. Congress: Addressing the Deficiencies" for the John T. Williams Dissertation Prize, 2018. At a high level, McAlister's work seeks to clarify and solve important issues that make inferences from traditional methods like NOMINATE problematic. These include our understanding of the dimensionality of a policy space, dependence across voting decisions, and the simultaneous identification of policy and status quo positions. His proposed solutions use developments in Bayesian nonparametrics from the machine learning literature; in particular, innovative nonparametric priors that allow for the creation of flexible theory testing tools. Furthermore, the proposed methods allow the analysis of roll calls to systematically include metadata from sources such as legislative texts and congressional committee structure. While the specific substantive scope of this dissertation is US politics, the techniques proposed in McAlister's dissertation plan provide a new and exciting approach to the measurement of abstract concepts in political science more broadly. We unanimously agree that this was a carefully written, agenda-setting prospectus.