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Should I Use Fixed or Random Effects?

Published online by Cambridge University Press:  21 November 2014


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.

Original Articles
Copyright © The European Political Science Association 2014 

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Tom Clark is Asa Griggs Candler Professor of Political Science, Emory University, 1555 Dickey Drive, Atlanta, GA 30030 USA (email: Drew Linzer is Assistant Professor, Department of Political Science, Emory University (email: 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.


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Clark and Linzer Dataset