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Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey

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  • Joseph W. Sakshaug
  • Trivellore E. Raghunathan

Abstract

Small area statistics obtained from sample survey data provide a critical source of information used to study health, economic, and sociological trends. However, most large-scale sample surveys are not designed for the purpose of producing small area statistics. Moreover, data disseminators are prevented from releasing public-use microdata for small geographic areas for disclosure reasons; thus, limiting the utility of the data they collect. This research evaluates a synthetic data method, intended for data disseminators, for releasing public-use microdata for small geographic areas based on complex sample survey data. The method replaces all observed survey values with synthetic (or imputed) values generated from a hierarchical Bayesian model that explicitly accounts for complex sample design features, including stratification, clustering, and sampling weights. The method is applied to restricted microdata from the National Health Interview Survey and synthetic data are generated for both sampled and non-sampled small areas. The analytic validity of the resulting small area inferences is assessed by direct comparison with the actual data, a simulation study, and a cross-validation study.

Suggested Citation

  • Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2103-2122, October.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2103-2122
    DOI: 10.1080/02664763.2014.909778
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    References listed on IDEAS

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    1. Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
    2. Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, January.
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    Cited by:

    1. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.

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