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