Open Access
May 2007 Struggles with Survey Weighting and Regression Modeling
Andrew Gelman
Statist. Sci. 22(2): 153-164 (May 2007). DOI: 10.1214/088342306000000691


The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of poststratification cells. It is then a challenge to develop general families of multilevel probability models that yield reasonable Bayesian inferences. We discuss in the context of several ongoing public health and social surveys. This work is currently open-ended, and we conclude with thoughts on how research could proceed to solve these problems.


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Andrew Gelman. "Struggles with Survey Weighting and Regression Modeling." Statist. Sci. 22 (2) 153 - 164, May 2007.


Published: May 2007
First available in Project Euclid: 27 September 2007

zbMATH: 1246.62043
MathSciNet: MR2408951
Digital Object Identifier: 10.1214/088342306000000691

Keywords: multilevel modeling , poststratification , sampling weights , shrinkage

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.22 • No. 2 • May 2007
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