title:         Struggles with survey weighting and regression modeling
authors:       Andrew Gelman
entrydate:     2005-10-12 17:23:58
keywords:      multilevel modeling, poststrati cation, sampling weights, shrinkage
abstract:      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 post-stratification
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.

http://polmeth.wustl.edu/retrieve.php?id=565

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