The Conference on Ideal Point Models will be held on May 1–2 at the Massachusetts Institute of Technology in Cambridge, Massachusetts, and is co-hosted by the University of Wisconsin–Madison and the University of Texas at Austin. The conference received funding from the Society for Political Methodology and the National Science Foundation as well as from the Massachusetts Institute of Technology. All reasonable travel costs for participants will be paid by the conference.
The conference is a thematic methodology meeting of the Society for Political Methodology and is intended to provide a forum for scholars to discuss recent advances in ideal point models, as well as continuing methodological challenges, in the development of IRT models for political science questions. The goal of thematic methodology meetings is to provide a smaller and more focused forum for scholarly exchange on a scale reminiscent of the original annual meetings of 25 years ago.
Spatial models have long been fundamental, even “iconic” (Brady 2011) tools of political science research, and are essential to many of the most important questions and theories in political science. New statistical techniques and data sources have enabled researchers to advance the frontiers of spatial modeling on a number of fronts. However, there are also many questions about how the identifying and bridging assumptions used in IRT models affect substantive inferences.
Techniques to bridge spatial models across time, institutions, and data types
While most ideal-point models are focused on a single institution or group of voters, some of the most important recent applications of spatial models have involved “bridging” the preferences of political actors in different contexts onto a common scale. Examples include producing estimates that are comparable across time, across institutions, or between elites and the mass public. Recently, however, some scholars have expressed concern that our ability to estimate models that bridge across time or context has outstripped our understanding of the assumptions that undergird these models. Given the importance of the research conclusions that hinge on these assumptions, it is crucial that scholars develop a shared set of standards for describing and evaluating assumptions for bridging spatial models.
Although early work on ideal-point estimation treated both bill and ideal-point parameters as non-random, subsequent work suggested a random effects approach to ideal-point estimation. A more recent line of research has sought to augment the standard ideal point model with additional information. For instance, various models have added hierarchies formed from partisanship, issue area, and topic models of bill text.
Nonparametric and flexible parametric estimation
Most approaches to ideal-point estimation assume a particular functional form of the utility curves and a particular distribution for the error terms. In recent years, some scholars have moved toward nonparametric approaches to ideal-point estimation. Another literature has retained a parametric framework but attempted to relax the parametric assumptions or distinguish between common choices. Both of these literatures have important implications for work on spatial models in political science.
Dimensionality of IRT Models
Dimensionality of IRT Models: There is no consensus in the literature on the best way to estimate the number of dimensions in a spatial model. The most common approach is to assess whether adding dimensions increases the percentage of items correctly classified by the model. But recent work in the congressional literature has shown that polarization of preferences can produce misleading evidence of low-dimensionality. We hope that this conference will help scholars discuss new ways to assess the dimensionality of spatial models.