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Abstract Details
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Activity Number:
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290
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Social Statistics Section
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| Abstract - #301224 |
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Title:
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Combining Information from Multiple Complex Surveys
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Author(s):
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Qi Dong*+
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Companies:
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University of Michigan
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Address:
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Program in Survey Methodology, Ann Arbor, MI, 48104,
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Keywords:
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complex sample survey ;
combining rule for multiple surveys ;
synthetic populations ;
multiple imputation ;
health insurance coverage rates ;
NHIS, MEPS
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Abstract:
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This article describes the use of multiple imputation to combine information from multiple surveys of the same underlying population. The basic proposal is to simulate synthetic populations from which the respondents of each survey have been selected. In this process, different sampling designs of the multiple surveys will be taken into account. Once we have the synthetic populations, we could treat them as simple random samples with no complex sampling design features and borrow information across surveys to adjust for nonsampling errors or fill in the variables that are lacking in one or more surveys. Then, we can analyze each synthetic population with standard complete-data software for simple random samples and obtain valid inference by combining the point and variance estimates first across synthetic populations within each survey using the existing combining rules for synthetic da
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Authors who are presenting talks have a * after their name.
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