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Should I Use Fixed or Random Effects?

Published online by Cambridge University Press:  21 November 2014

Tom S. Clark  and
Drew A. Linzer
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Abstract

Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed- or random-effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. This study performs a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. The results offer a typology of dataset characteristics to help researchers choose a preferred model.

Type
Original Articles
Information
Political Science Research and Methods , Volume 3 , Issue 2 , May 2015 , pp. 399 - 408
Copyright
Copyright © The European Political Science Association 2014 

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Footnotes

*

Tom Clark is Asa Griggs Candler Professor of Political Science, Emory University, 1555 Dickey Drive, Atlanta, GA 30030 USA (email: tom.clark@emory.edu). Drew Linzer is Assistant Professor, Department of Political Science, Emory University (email: drew@votamatic.org). We thank Kyle Beardsley, Justin Esarey, Andrew Gelman, Kosuke Imai, Benjamin Lauderdale, Jeffrey Lax and Jamie Monogan for helpful discussions and feedback. Nigel Lo provided valuable research assistance.

References

Angrist, Joshua D., and Pischke, Jörn-Steffen. 2009. Mostly Harmless Econometrics. Princeton, NJ: Princeton University Press.Google Scholar
Arceneaux, Kevin, and Nickerson, David W.. 2009. ‘Modeling Certainty with Clustered Data: A Comparison of Methods’. Political Analysis 17(2):177–190.Google Scholar
Bates, Douglas, Maechler, Martin, and Bolker, Ben. 2011. lme4: Linear Mixed-effects Models Wsing S4 Classes. R package version 0.999375-38. Available from http://cran.R-project.org/package=lme4 Google Scholar
Beck, Nathaniel, and Katz, Jonathan N.. 1995. ‘What To Do (and not To Do) with Time-Series Cross-Section Data’. American Political Science Review 89(3):634–647.Google Scholar
Frees, Edward W. 2004. Longitudinal and Panel Data: Analysis and Applications in the Social Sciences. New York: Cambridge University Press.Google Scholar
Gelman, Andrew. 2005. ‘Analysis of Variance—Why It is More Important than Ever’. The Annals of Statistics 33(1):1–53.Google Scholar
Gelman, Andrew, and Hill, Jennifer. 2007. Data Analysis Using Regression and Multi-level/Hierarchical Models. Cambridge: Cambridge University Press.Google Scholar
Greene, William H. 2012. Econometric Analysis, Seventh Edition. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hausman, Jerry A. 1978. ‘Specification Tests in Econometrics’. Econometrica 46:1251–1271.Google Scholar
Kennedy, Peter E. 2003. A Guide to Econometrics. Cambridge, MA: MIT Press.Google Scholar
Kreft, Ita G.G., and de DeLeeuw, Jan. 1998. Introducing Multilevel Modeling. London: Sage.Google Scholar
Plümper, 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):124–139.Google Scholar
Plümper, Thomas, and Troeger, Vera E.. 2011. ‘Fixed-Effects Vector Decomposition: Properties, Reliability, and Instruments’. Political Analysis 19(2):147–164.Google Scholar
R Development Core Team. 2014. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. http://www.R-project.org Google Scholar
Robinson, G.K.. 1998. ‘Variance Components’. In Encyclopedia of Biostatistics, edited by Peter Armitage and Theodore Colton, Vol. 6, 4713–4719. Wiley.Google Scholar
Wilson, Sven E., and Butler, Daniel M.. 2007. ‘A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications’. Political Analysis 15(2):101–123.Google Scholar
Wooldridge, Jeffrey M.. 2010. Econometric Analysis of Cross Section and Panel Data. 2nd edition. Cambridge, MA: MIT Press.Google Scholar
Supplementary material: Link

Clark and Linzer Dataset

Dataset

http://dx.doi.org/10.7910/DVN/25588
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