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Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls

Published online by Cambridge University Press:  04 January 2017

David K. Park ,
Andrew Gelman  and
Joseph Bafumi
David K. Park
Affiliation:
Department of Political Science and Applied Statistics, Washington University, St. Louis, MO 63130. e-mail: dpark@artsci.wustl.edu
Andrew Gelman
Affiliation:
Departments of Statistics and Political Science, Columbia University, New York, NY 10027. e-mail: gelman@stat.columbia.edu
Joseph Bafumi
Affiliation:
Department of Political Science, Columbia University, New York, NY 10027
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Abstract

We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997, Survey Methodology 23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the usual demographic variables. We evaluate the model by comparing it to state-level election outcomes. The multilevel model outperforms more commonly used models in political science. We envision the most important usage of this method to be not forecasting elections but estimating public opinion on a variety of issues at the state level.

Type
Research Article
Information
Political Analysis , Volume 12 , Issue 4: Special Issue on Bayesian Methods , Autumn 2004 , pp. 375 - 385
Copyright
Copyright © Society for Political Methodology 2004 

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