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Multiple imputation for combining confidential data owned by two agencies

Author

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  • Christine N. Kohnen
  • Jerome P. Reiter

Abstract

Summary. Statistical agencies that own different databases on overlapping subjects can benefit greatly from combining their data. These benefits are passed on to secondary data analysts when the combined data are disseminated to the public. Sometimes combining data across agencies or sharing these data with the public is not possible: one or both of these actions may break promises of confidentiality that have been given to data subjects. We describe an approach that is based on two stages of multiple imputation that facilitates data sharing and dissemination under restrictions of confidentiality. We present new inferential methods that properly account for the uncertainty that is caused by the two stages of imputation. We illustrate the approach by using artificial and genuine data.

Suggested Citation

  • Christine N. Kohnen & Jerome P. Reiter, 2009. "Multiple imputation for combining confidential data owned by two agencies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 511-528, April.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:2:p:511-528
    DOI: 10.1111/j.1467-985X.2008.00574.x
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    References listed on IDEAS

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    1. Ghosh, Joyee & Reiter, Jerome P. & Karr, Alan F., 2007. "Secure computation with horizontally partitioned data using adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5813-5820, August.
    2. 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.
    3. 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.
    4. Di An & Roderick J. A. Little, 2007. "Multiple imputation: an alternative to top coding for statistical disclosure control," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 923-940, October.
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    Cited by:

    1. Chipperfield James O., 2014. "Disclosure-Protected Inference with Linked Microdata Using a Remote Analysis Server," Journal of Official Statistics, Sciendo, vol. 30(1), pages 123-146, March.
    2. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    3. Whittaker, Gerald & Färe, Rolf & Grosskopf, Shawna & Barnhart, Bradley & Bostian, Moriah & Mueller-Warrant, George & Griffith, Stephen, 2017. "Spatial targeting of agri-environmental policy using bilevel evolutionary optimization," Omega, Elsevier, vol. 66(PA), pages 15-27.
    4. Dong Hua & Meeden Glen, 2016. "Constructing Synthetic Samples," Journal of Official Statistics, Sciendo, vol. 32(1), pages 113-127, March.

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