Combining information from multiple complex surveys

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Qi Dong, Michael R. Elliott and Trivellore E. Raghunathan^Note 1

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Abstract

This manuscript describes the use of multiple imputation to combine information from multiple surveys of the same underlying population. We use a newly developed method to generate synthetic populations nonparametrically using a finite population Bayesian bootstrap that automatically accounting for complex sample designs. We then analyze each synthetic population with standard complete-data software for simple random samples and obtain valid inference by combining the point and variance estimates using extensions of existing combining rules for synthetic data. We illustrate the approach by combining data from the 2006 National Health Interview Survey (NHIS) and the 2006 Medical Expenditure Panel Survey (MEPS).

Key Words: Synthetic populations; Posterior predictive distribution; Bayesian bootstrap; Inverse sampling.

Table of content

• 1. Introduction
• 2. Generating synthetic populations from single survey data that accounts for complex sampling designs
• 3. Combining rule for the synthetic populations from multiple surveys
• 4. Combined estimates of health insurance coverage from the NHIS, MEPS and BRFSS
• 5. Discussion
• References

Notes