Abstract #300187

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JSM 2003 Abstract #300187
Activity Number: 321
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract - #300187
Title: A Bayesian Hierarchical Approach to Modeling Spatial Variation of the Ohio Debt Stress Index with Missing Addresses
Author(s): Xiang Ling*+
Companies: The Ohio State University
Address: 605 Tuscarawas Ct., Columbus, OH, 43210,
Keywords: Bayesian methods ; Geographic Information System (GIS) ; Markov chain Monte Carlo methods ; missing data ; RDD survey
Abstract:

Missing data is a common problem in survey research, and more sensitive items are more likely to be missing. In particular, respondents in an RDD survey may be reluctant to report their addresses. This lack of location information is different from the usual missing data problem in spatial analysis, where the location is known and the measurement is missing. Because of this "backwards" nature, traditional methods to adjust for or impute the missing data are not appropriate or efficient. We present some adaptations of standard missing-data methods to the spatial analysis setting and a Bayesian hierarchical approach which relies on Markov chain Monte Carlo to solving the missing address problem.


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