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A calibrated imputation method for secondary data analysis of survey data

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  • Damião N. Da Silva
  • Li‐Chun Zhang

Abstract

In practical survey sampling, missing data are unavoidable due to nonresponse, rejected observations by editing, disclosure control, or outlier suppression. We propose a calibrated imputation approach so that valid point and variance estimates of the population (or domain) totals can be computed by the secondary users using simple complete‐sample formulae. This is especially helpful for variance estimation, which generally require additional information and tools that are unavailable to the secondary users. Our approach is natural for continuous variables, where the estimation may be either based on reweighting or imputation, including possibly their outlier‐robust extensions. We also propose a multivariate procedure to accommodate the estimation of the covariance matrix between estimated population totals, which facilitates variance estimation of the ratios or differences among the estimated totals. We illustrate the proposed approach using simulation data in supplementary materials that are available online.

Suggested Citation

  • Damião N. Da Silva & Li‐Chun Zhang, 2021. "A calibrated imputation method for secondary data analysis of survey data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 25-41, March.
    • Handle: RePEc:bla:scjsta:v:48:y:2021:i:1:p:25-41
    DOI: 10.1111/sjos.12435
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    References listed on IDEAS

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    1. Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    2. Jae Kwang Kim & J. N. K. Rao, 2009. "A unified approach to linearization variance estimation from survey data after imputation for item nonresponse," Biometrika, Biometrika Trust, vol. 96(4), pages 917-932.
    3. Jean‐François Beaumont, 2005. "Calibrated imputation in surveys under a quasi‐model‐assisted approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 445-458, June.
    4. Gelein, Brigitte & Haziza, David & Causeur, David, 2014. "Preserving relationships between variables with MIVQUE based imputation for missing survey data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 197-208.
    5. G. Chauvet & J.-C. Deville & D. Haziza, 2011. "On balanced random imputation in surveys," Biometrika, Biometrika Trust, vol. 98(2), pages 459-471.
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