Spatial models of choice and judgement

The course examines scaling methods and ideal point estimation, with a focus on methods derived from political spatial models applied to political choice data (e.g., legislative roll call and public opinion survey data). This course demonstrates how to interpret data via ideal point estimation and scaling methods using a variety of methods within the open-source programming language R. No previous knowledge of R is necessary and the course includes an introduction.

The course first covers how to analyze data from issue scales, focusing on surveys that ask respondents to place themselves and/or stimuli on issue or attribute scales. The course demonstrates how issue scales can be analyzed using both the Aldrich-McKelvey scaling and the ‘basic space’ method. The course next examines similarities and dissimilarities data, where entries represent the level of similarity or dissimilarity between objects (as in a correlation matrix). This part covers multidimensional scaling (MDS), specifically the SMACOF (Scaling by Majorizing a Complicated Function) optimization method implemented in R as well as Bayesian applications to metric MDS. Next, the course covers unfolding analysis of rating scale data such as feeling thermometers in which respondents place a politician or group on favorability scale. Finally, the course covers the unfolding of binary choice data such as legislative roll call voting. We will discuss Poole and Rosenthal’s W-NOMINATE and Poole’s Optimal Classification unfolding method, as well as Bayesian analysis of binary and ordinal choice data using Item Response Theory (IRT) implemented by Jackman’s pscl and Martin and Quinn’s MCMCpack.

Target audience: Students interested in deriving spatial preference information and dimensional structure from various types of political choice data, especially parliamentary voting and surveys relevant to legislative studies and political behavior.  Consumers of research based on these methods will also benefit from a deeper understanding of this type of research and its limitations. The course will be offered in English.