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

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  • Caughey, Devin
  • Warshaw, Christopher

Abstract

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.

Suggested Citation

  • Caughey, Devin & Warshaw, Christopher, 2015. "Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model," Political Analysis, Cambridge University Press, vol. 23(2), pages 197-211, April.
    • Handle: RePEc:cup:polals:v:23:y:2015:i:02:p:197-211_01
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    Cited by:

    1. Tinghua Yu & Elliott Ash, 2021. "Polarization and Political Selection," BCAM Working Papers 2105, Birkbeck Centre for Applied Macroeconomics.
    2. Sung-Geun Kim, 2023. "What can we talk about social cohesion in Korea? An item response theory approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2409-2427, June.
    3. Devin Caughey & James Dunham & Christopher Warshaw, 2018. "The ideological nationalization of partisan subconstituencies in the American States," Public Choice, Springer, vol. 176(1), pages 133-151, July.
    4. Joshua C. Hall & Donald J. Lacombe & Amir Neto & James Young, 2022. "Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 360-373, April.
    5. Michele Scotto di Vettimo, 2022. "Measuring public support for European integration using a Bayesian item response theory model," European Union Politics, , vol. 23(2), pages 171-191, June.
    6. Scott J. LaCombe, 2021. "Measuring Institutional Design in U.S. States," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1511-1533, July.
    7. John B. Holmes & Matthew R. Schofield & Richard J. Barker, 2022. "Pólya‐gamma data augmentation and latent variable models for multivariate binomial data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 194-218, January.

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