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Activity Number: 120
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #315751 View Presentation
Title: Multilevel Regression and Post-Stratification for Survey Weighting
Author(s): Yajuan Si* and Andrew Gelman
Companies: University of Wisconsin - Madison and Columbia University
Keywords: Survey weighting ; Multilevel regression ; Poststratification ; Bayesian ; Stan
Abstract:

Survey weighting adjusts for differences between the collected samples and the target population. However, classical weights have lots of problems. Extreme values of weights will cause high variability and blow up the estimates. In practice, weighting construction requires arbitrary choices about selection of weighting factors and interactions, pooling of weighting cells and weight trimming. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion, which are the variables used in survey weighting. We would like to incorporate these weighting variables into the model for survey outcomes under the framework of multilevel regression and poststratification at much finer levels. Our procedure will yield the model-based weights after smoothing. We will use Stan for computation and illustrate the performances via the application of the New York Longitudinal Survey of Poverty study.


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