JSM 2004 - Toronto

Abstract #300065

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Activity Number: 286
Type: Invited
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #300065
Title: Challenges in Bayesian Data Analysis for Survey Data
Author(s): Andrew Gelman*+ and John B. Carlin
Companies: Columbia University and Royal Children's Hospital and University of Melbourne
Address: Dept. of Statistics, New York, NY, 10027,
Keywords: multilevel modeling ; probability of inclusion ; nonresponse ; clustering ; survey weighting ; post-stratification
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.

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Revised March 2004