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Calibrated propensity score method for survey nonresponse in cluster sampling.

Jae Kwang Kim1, Yongchan Kwon2, Myunghee Cho Paik2

  • 1Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A.

Biometrika
|June 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a robust weighting adjustment method for survey sampling to address cluster-specific nonresponse. The approach ensures consistent estimation even if models are misspecified, improving data accuracy in complex surveys.

Keywords:
Calibration estimationNonignorable missingnessSurvey samplingWeighting

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Area of Science:

  • Statistics
  • Survey Methodology
  • Biostatistics

Background:

  • Weighting adjustment is crucial for correcting unit nonresponse in survey sampling.
  • Cluster sampling presents unique challenges due to within-cluster correlations and cluster-specific nonignorable missingness.

Purpose of the Study:

  • To propose a novel weighting adjustment method for cluster sampling with cluster-specific nonignorable missingness.
  • To develop a consistent estimator for means and totals under generalized linear mixed-effects models.

Main Methods:

  • A parametric working model for the response mechanism incorporating cluster-specific nonignorable missingness was developed.
  • A consistent estimator for the mean or totals was derived.
  • A consistent variance estimator using Taylor linearization was proposed.

Main Results:

  • The proposed weighting adjustment method provides a consistent estimator for the mean or totals.
  • The method is robust, ensuring estimator consistency even with misspecified response or outcome models.
  • Simulation studies and a real-data application demonstrated the method's effectiveness.

Conclusions:

  • The developed weighting adjustment method effectively addresses nonresponse in cluster sampling.
  • The robustness of the method enhances its applicability in complex survey data analysis.
  • The proposed techniques offer reliable estimation and variance calculation for survey data.