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Modeling Certainty with Clustered Data: A Comparison of Methods

Published online by Cambridge University Press:  04 January 2017

Kevin Arceneaux  and
David W. Nickerson
Kevin Arceneaux*
Affiliation:
Department of Political Science, Institute for Public Affairs, Faculty Affiliate, Temple University, 453 Gladfelter Hall, 1115 West Berks Street, Philadelphia, PA 19122
David W. Nickerson
Affiliation:
Department of Political Science, University of Notre Dame, 217 O'Shaughnessy Hall, Notre Dame, IN 46556
*
e-mail: [email protected] (corresponding author)
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Abstract

Political scientists often analyze data in which the observational units are clustered into politically or socially meaningful groups with an interest in estimating the effects that group-level factors have on individual-level behavior. Even in the presence of low levels of intracluster correlation, it is well known among statisticians that ignoring the clustered nature of such data overstates the precision estimates for group-level effects. Although a number of methods that account for clustering are available, their precision estimates are poorly understood, making it difficult for researchers to choose among approaches. In this paper, we explicate and compare commonly used methods (clustered robust standard errors (SEs), random effects, hierarchical linear model, and aggregated ordinary least squares) of estimating the SEs for group-level effects. We demonstrate analytically and with the help of empirical examples that under ideal conditions there is no meaningful difference in the SEs generated by these methods. We conclude with advice on the ways in which analysts can increase the efficiency of clustered designs.

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 2 , Spring 2009 , pp. 177 - 190
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We would like to thank Bob Erikson, Chris Zorn, and the anonymous reviewers for helpful comments and suggestions. We would also like to thank Robert Brown and Lawrence Broz for generously sharing data. All errors remain our own.

References

Arceneaux, Kevin. 2005. “Using cluster randomized field experiments to study voting behavior.” Annals of the American Academy of Political and Social Science 601: 169–79.Google Scholar
Beck, Nathaniel, and Katz, Jonathan. 2001. “Throwing out the baby with the bath water. A comment on Green, Kim, and Yoon.” International Organization 55: 487–95.Google Scholar
Brown, Robert D., Jackson, Robert A., and Wright, Gerald C. 1999. “Registration, turnout, and state party systems.” Political Research Quarterly 52(3): 463–79.Google Scholar
Broz, J. Lawrence. 2002. “Political system transparency and monetary commitment regimes.” International Organization 56(4): 861–87.Google Scholar
Cornfeld, J. 1978. “Randomization by group: a formal analysis.” American Journal of Epidemiology 108: 100–2.Google Scholar
Donner, Allan. 1998. “Some aspects of the design and analysis of cluster randomized trials.” Applied Statistics 47: 95–113.Google Scholar
Donner, Allan, and Klar, Neil. 2000. Design and analysis of cluster randomization trials in health research. New York: Arnold Publishers.Google Scholar
Green, Donald P., Kim, Soo Yeon, and Yoon, David H. 2001. “Dirty pool.” International Organization 55: 441–68.Google Scholar
Green, Donald P., and Vavreck, Lynn. 2008. “Analysis of cluster-randomized field experiments: a comparison of alternative estimation approaches.” Political Analysis 16(2): 138–52.CrossRefGoogle Scholar
Greene, William H. 2000. Econometric analysis, fourth edition. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hill, Kim Quaile, and Leighley, Jan E. 1993. “Party ideology, organization, and competitiveness as mobilizing forces in Gubernatorial Elections.” American Journal of Political Science 37: 1158–78.CrossRefGoogle Scholar
Hill, Kim Quaile, and Leighley, Jan E. 1996. “Political parties and class mobilization in contemporary United States elections.” American Journal of Political Science 40: 787–804.CrossRefGoogle Scholar
Kish, Leslie. 1965. Survey sampling. New York: Wiley.Google Scholar
Kmenta, Jan. 1997. Elements of econometrics: second edition. Ann Arbor: University of Michigan Press.CrossRefGoogle Scholar
Murray, David M. 1998. The design and analysis of group-randomized trials. New York: Oxford University Press.Google Scholar
Plumper, Thomas, and Troeger, Vera E. 2007. “Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects.” Political Analysis 15(2): 124–39.Google Scholar
Rabe-Hesketh, Sophia, and Skrondal, Anders. 2005. Multilevel and longitudinal modeling using Stata. College Station, TX: Stata Press.Google Scholar
Raudenbush, Stephen W., and Bryk, Anthony S. 2002. Hierarchical linear models: applications and data analysis methods. 2nd edn. Thousand Oaks, CA: Sage Publications.Google Scholar
Steenbergen, Marco R., and Jones, Bradford. 2002. “Modelling multilevel data structures.” American Journal of Political Science 46: 218–37.Google Scholar
Stoker, Laura, and Bowers, Jake. 2002. “Designing multi-level studies: sampling voters and electoral contexts.” Electoral Studies 21: 235–67.Google Scholar
Timpone, Richard J. 1998. “Structure, behavior, and voter turnout in the United States.” American Political Science Review 92: 145–58.Google Scholar
Zorn, Christopher. 2006. “Comparing GEE and robust standard errors for conditionally dependent data.” Political Research Quarterly 59(3): 329–41.CrossRefGoogle Scholar