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Estimating Smooth Country–Year Panels of Public Opinion

Published online by Cambridge University Press:  04 July 2018

Christopher Claassen Open the ORCID record for Christopher Claassen [Opens in a new window]
Christopher Claassen*
Affiliation:
School of Social and Political Sciences, University of Glasgow, Glasgow G12 8QQ, UK. Email: christopher.claassen@glasgow.ac.uk
*
*Email: christopher.claassen@glasgow.ac.uk
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Abstract

At the microlevel, comparative public opinion data are abundant. But at the macrolevel—the level where many prominent hypotheses in political behavior are believed to operate—data are scarce. In response, this paper develops a Bayesian dynamic latent trait modeling framework for measuring smooth country–year panels of public opinion even when data are fragmented across time, space, and survey item. Six models are derived from this framework, applied to opinion data on support for democracy, and validated using tests of internal, external, construct, and convergent validity. The best model is reasonably accurate, with predicted responses that deviate from the true response proportions in a held-out test dataset by 6 percentage points. In addition, the smoothed country–year estimates of support for democracy have both construct and convergent validity, with spatiotemporal patterns and associations with other covariates that are consistent with previous research.

Keywords

latent variablestime serieshierarchical modelingBayesian estimationpublic opinionsupport for democracy

Information

Type
Articles
Information
Political Analysis , Volume 27 , Issue 1 , January 2019 , pp. 1 - 20
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Author’s note: I am grateful for the helpful comments provided by Devin Caughey, Roberto Stefan Foa, Duncan Lee, Anthony J. McGann, Jamie Monogan, and Richard Traunmüller on earlier versions of this paper. I acknowledge the financial support of the Carnegie Trust for the Universities of Scotland and the Adam Smith Research Foundation at the University of Glasgow. Finally, I appreciate the research assistance provided by Jose Ricardo Villanueva Lira and Bryony MacLeod. Replication materials are provided in the Political Analysis dataverse (Claassen 2018).

Contributing Editor: Jonathan N. Katz

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