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Combining information from multiple complex surveys.

Qi Dong1, Michael R Elliott2, Trivellore E Raghunathan3

  • 1Google, Inc., 1R4A, Quad 5, Google Inc, 399 N. Whisman Road, Mountain View, CA 94043. qdong@google.com.

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Summary
This summary is machine-generated.

This study introduces a new method for combining data from multiple surveys using synthetic populations. This approach ensures valid statistical inference even with complex sample designs.

Keywords:
Bayesian bootstrapInverse samplingPosterior predictive distributionSynthetic populations

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Area of Science:

  • Statistics
  • Survey Methodology
  • Data Science

Background:

  • Combining data from multiple surveys is crucial for robust population analysis.
  • Existing methods for data integration often struggle with complex sample designs.
  • The National Health Interview Survey (NHIS) and Medical Expenditure Panel Survey (MEPS) are key national health surveys.

Purpose of the Study:

  • To present a novel nonparametric method for generating synthetic populations.
  • To enable the combination of information from multiple complex-sample surveys.
  • To facilitate valid statistical inference from integrated survey data.

Main Methods:

  • Utilizes a finite population Bayesian bootstrap for nonparametric synthetic population generation.
  • Accounts for complex sample designs automatically during synthetic data creation.
  • Employs extensions of combining rules for synthetic data to merge estimates.

Main Results:

  • Successfully generated synthetic populations that reflect complex survey designs.
  • Demonstrated valid inference by combining point and variance estimates.
  • Illustrated the method's application using the 2006 NHIS and MEPS datasets.

Conclusions:

  • The developed method offers a powerful tool for integrating data from multiple complex-sample surveys.
  • This approach allows for the use of standard complete-data analysis software.
  • Valid statistical inference can be achieved when combining survey data using synthetic populations.