JSM 2011 Online Program

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Abstract Details

Activity Number: 154
Type: Invited
Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
Sponsor: General Methodology
Abstract - #300200
Title: Post-Stratification and Network Sampling
Author(s): Rachel Schutt and Andrew Gelman*+ and Tyler McCormick
Companies: Google Inc. and Columbia University and University of Washington
Address: Department of Statistics, New York, NY, 10027, USA
Keywords: Hierarchical modeling ; post-stratification ; network sampling

We propose a method for adjusting for bias in samples from networks using Bayesian hierarchical models. Our method combines previous work in post-stratification for standard surveys with recent work on network sampling and indirectly observed network data. A key feature of our approach is incorporating network structure into the hierarchical model to reduce bias in population or subpopulation-level estimates. We demonstrate our general framework using a sample of 500 men who have sex with men recruited using Respondent Driven Sampling in Buenos Aires, Argentina.

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