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Applying a Two-Step Strategy to the Analysis of Cross-National Public Opinion Data

Published online by Cambridge University Press:  04 January 2017

Karen Long Jusko  and
W. Phillips Shively
Karen Long Jusko*
Affiliation:
Department of Political Science, University of Michigan, 5700 Haven Hall, 505 South State Street, Ann Arbor, MI 48109–1045
W. Phillips Shively
Affiliation:
Department of Political Science, University of Minnesota, 1414 Social Sciences Bldg. 267 19th Ave. South, Minneapolis, MN 55455. email: [email protected]
*
email: [email protected] (corresponding author)
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Abstract

In recent years, large sets of national surveys with shared content have increasingly been used for cross-national opinion research. But scholars have not yet settled on the most flexible and efficient models for utilizing such data. We present a two-step strategy for such analysis that takes advantage of the fact that in such datasets each “cluster” (i.e., country sample) is large enough to sustain separate analysis of its internal variances and covariances. We illustrate the method by examining a puzzle of comparative electoral behavior—why does turnout decline rather than increase with the number of parties competing in an election (Blais and Dobryzynska 1998, for example)? This discussion demonstrates the ease with which a two-step strategy incorporates confounding variables operating at different levels of analysis. Technical appendices demonstrate that the two-step strategy does not lose efficiency of estimation as compared with a pooling strategy.

Type
Research Article
Information
Political Analysis , Volume 13 , Issue 4: Special Issue on Multilevel Modeling for Large Clusters , Autumn 2005 , pp. 327 - 344
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We are grateful for the comments and suggestions offered by the participants of the Princeton University conference on hierarchical models, especially Chris Achen and Larry Bartels.

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