Skip to main content
×
Home
    • Aa
    • Aa

Modeling Certainty with Clustered Data: A Comparison of Methods

  • Kevin Arceneaux (a1) and David W. Nickerson (a2)
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.

Copyright
Corresponding author
e-mail: kevin.arceneaux@temple.edu (corresponding author)
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

Kevin Arceneaux . 2005. “Using cluster randomized field experiments to study voting behavior.” Annals of the American Academy of Political and Social Science 601: 169–79.

Robert D. Brown , Robert A. Jackson , and Gerald C. Wright 1999. “Registration, turnout, and state party systems.” Political Research Quarterly 52(3): 463–79.

Kim Quaile Hill , and Jan E. Leighley 1993. “Party ideology, organization, and competitiveness as mobilizing forces in Gubernatorial Elections.” American Journal of Political Science 37: 1158–78.

Kim Quaile Hill , and Jan E. Leighley 1996. “Political parties and class mobilization in contemporary United States elections.” American Journal of Political Science 40: 787804.

Jan Kmenta . 1997. Elements of econometrics: second edition. Ann Arbor: University of Michigan Press.

Marco R. Steenbergen , and Bradford Jones . 2002. “Modelling multilevel data structures.” American Journal of Political Science 46: 218–37.

Laura Stoker , and Jake Bowers . 2002. “Designing multi-level studies: sampling voters and electoral contexts.” Electoral Studies 21: 235–67.

Christopher Zorn . 2006. “Comparing GEE and robust standard errors for conditionally dependent data.” Political Research Quarterly 59(3): 329–41.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×
MathJax

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 4 *
Loading metrics...

Abstract views

Total abstract views: 33 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 30th April 2017. This data will be updated every 24 hours.