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Abstract Details

Activity Number: 583
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #303957
Title: A Two-Step Semi-Parametric Method to Account for Survey Weights in Multiple Imputation
Author(s): Hanzhi Zhou*+
Companies: University of Michigan
Address: 645 Hidden Valley Club Drive, Ann Arbor, MI, 48104-6797, United States
Keywords: missing data ; complex sampling design ; multiple imputation ; Bayesian Bootstrap ; synthetic data ; Pólya Posterior

Multiple imputation (MI) is a principled method in dealing with item-level missing data and has become increasingly popular in the public health and social science investigations where data production is often based on complex sample surveys. However, existing software packages and procedures typically do not incorporate complex sample design features in the imputation process. Failure to account for design features, particularly sampling weights, can introduce bias on final estimates and hence invalid inference. Recent work to accommodate complex sample designs (including clustering and stratification) in imputation includes the sample design in the formulation of the imputation model, which typically requires strong model assumptions and can involve expensive computation in practice. In this paper, we propose a new method to incorporate complex sample designs in MI. Specifically, we divide the imputation process into two steps: the complex feature of the survey design (unequal probability selection in particular) is fully accounted for at the first step, which is accomplished by applying nonparametric methods to generate a series of synthetic datasets; we then perform conventional parametric MI for missing data at the second step using readily available imputation software designed for an SRS sample. A new combining rule for the point and variance estimates is derived to make valid inferences based on the two-step procedure. We evaluate the performance of the new method in comparison with the fully model-based method through a simulation design. Results show that the new method is more robust to model misspecification and generally yields lower RMSE than the fully model-based method.

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