Calibrated propensity score method for survey nonresponse in cluster sampling

• 成果类型: Article
• 署名作者: Kim, Jae Kwang; Kwon, Yongchan; Paik, Myunghee Cho
• 署名单位: Iowa State University; Seoul National University (SNU)
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asw004
• 发表日期: 2016
• 页码: 461473
• 关键词: parametric fractional imputation; Missing Data; Causal Inference; incomplete data; MODEL; Robustness

摘要

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.