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Calibrated propensity score method for survey nonresponse in cluster sampling

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  • Jae Kwang Kim
  • Yongchan Kwon
  • Myunghee Cho Paik

Abstract

Weighting adjustment is commonly used in survey sampling to correct for unit nonresponse. In cluster sampling, the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster-specific nonignorable missingness. Based on a parametric working model for the response mechanism that incorporates cluster-specific nonignorable missingness, we propose a method of weighting adjustment. We provide a consistent estimator of the mean or totals in cases where the study variable follows a generalized linear mixed-effects model. The proposed method is robust in the sense that the consistency of the estimator does not require correct specification of the functional forms of the response and outcome models. A consistent variance estimator based on Taylor linearization is also proposed. Numerical results, including a simulation and a real-data application, are presented.

Suggested Citation

  • Jae Kwang Kim & Yongchan Kwon & Myunghee Cho Paik, 2016. "Calibrated propensity score method for survey nonresponse in cluster sampling," Biometrika, Biometrika Trust, vol. 103(2), pages 461-473.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:2:p:461-473.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw004
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    Cited by:

    1. Nuanpan Lawson & Chris Skinner, 2017. "Estimation of a cluster-level regression model under nonresponse within clusters," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 319-331, December.
    2. Shagaida, Natalia (Шагайда, Наталья), 2016. "Mechanisms of State Regulation of the Use of Agricultural Land for Construction Purposes: International and Russian Experience [Механизмы Государственного Регулирования Использования Сельскохозяйст," Working Papers 2864, Russian Presidential Academy of National Economy and Public Administration.
    3. Kim, Gi-Soo & Paik, Myunghee Cho & Kim, Hongsoo, 2017. "Causal inference with observational data under cluster-specific non-ignorable assignment mechanism," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 88-99.

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