Should I Use Fixed or Random Effects?

Empirical analyses in political science very commonly confront data that are grouped— multiple votes by individual legislators, multiple years in individual states, multiple conflicts during individual years, and so forth. Modeling these data presents a series of potential challenges, of which accounting for differences across the groups is perhaps the most well-known. Two widely-used methods are the use of either “fixed” or “random” effects models. However, how best to choose between these approaches remains unclear in the applied literature. We employ a series of simulation experiments to evaluate the relative performance of fixed and random effects estimators for varying types of datasets. We further investigate the commonly-used Hausman test, and demonstrate that it is neither a necessary nor sufficient statistic for deciding between fixed and random effects. We summarize the results into a typology of datasets to offer practical guidance to the applied researcher. Acknowledgements: We thank Kyle Beardsley, Justin Esarey, Andrew Gelman, Benjamin Lauderdale, Jeffrey Lax, and Jamie Monogan for helpful discussions and feedback. Nigel Lo provided valuable research assistance.