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Practical and Effective Approaches to Dealing With Clustered Data

Published online by Cambridge University Press:  19 January 2018


Cluster-robust standard errors (as implemented by the eponymous cluster option in Stata) can produce misleading inferences when the number of clusters G is small, even if the model is consistent and there are many observations in each cluster. Nevertheless, political scientists commonly employ this method in data sets with few clusters. The contributions of this paper are: (a) developing new and easy-to-use Stata and R packages that implement alternative uncertainty measures robust to small G, and (b) explaining and providing evidence for the advantages of these alternatives, especially cluster-adjusted t-statistics based on Ibragimov and Müller. To illustrate these advantages, we reanalyze recent work where results are based on cluster-robust standard errors.

Original Articles
© The European Political Science Association 2018 

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Justin Esarey is an Assistant Professor of Political Science, Rice University, 6100 Main St, MS-24, Houston, TX 77005 ( Andrew Menger, Ph.D. Candidate, Department of Political Science, Rice University, 6100 Main St, MS-24, Houston, TX 77005 ( The authors thank Ulrich Müller, Carlisle Rainey, Jonathan Kropko, Matthew Webb, Neal Beck, Jens Hainmueller, Shuai Jin, Jens Grosser, Ernesto Reuben, our anonymous reviewers, and participants at the 2015 Annual Meeting of the Midwest Political Science Association, the 2015 Annual Meeting of the Society for Political Methodology, and the 2016 Annual Meeting of the Southern Political Science Association for helpful comments and suggestions on earlier drafts of this paper. To view supplementary material for this article, please visit


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