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Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model

  • Devin Caughey (a1) and Christopher Warshaw (a2)


Over the past eight decades, millions of people have been surveyed on their political opinions. Until recently, however, polls rarely included enough questions in a given domain to apply scaling techniques such as IRT models at the individual level, preventing scholars from taking full advantage of historical survey data. To address this problem, we develop a Bayesian group-level IRT approach that models latent traits at the level of demographic and/or geographic groups rather than individuals. We use a hierarchical model to borrow strength cross-sectionally and dynamic linear models to do so across time. The group-level estimates can be weighted to generate estimates for geographic units. This framework opens up vast new areas of research on historical public opinion, especially at the subnational level. We illustrate this potential by estimating the average policy liberalism of citizens in each U.S. state in each year between 1972 and 2012.


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Authors' note: We are grateful to Kevin Quinn, Simon Jackman, and Teppei Yamamoto for their advice on the model derivation and validation, and to Bob Carpenter and Alex Storer for their assistance with coding and computation. We also received excellent feedback from Stephen Jessee, Bob Erikson, Mike Alvarez, John Jackson, and others at PolMeth 2013. Adam Berinsky, Eric Schickler, and Tom Clark were kind enough to share their data with us. We appreciate the research assistance of Stephen Brown, Justin de Benedictis-Kessner, and Melissa Meek. Supplementary materials for this article are available on the Political Analysis Web site.



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Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model

  • Devin Caughey (a1) and Christopher Warshaw (a2)


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