Multiple Imputation Using the Weighted Finite Population Bayesian Bootstrap

• Activity Number: 167
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Survey Research Methods Section
• Author(s): Michael Elliott* and Hanzhi Zhou and Trivellore Raghunathan
• Companies: University of Michigan and Mathematica Policy Research and University of Michigan
• Keywords: Finite population Bayesian bootstrap ; Haldane prior ; clustered sample ; sample weights

Abstract

Accounting for multistage survey sample design features when generating datasets for multiple imputation is a non-trivial task. Thus, multiple imputation often ignores complex sample designs and assumes simple random sampling when generated imputations, even though failing to account for complex sample design features is known to damage inference. Here we extend a recently-developed weighted finite population Bayesian bootstrap procedure (Dong et al. 2014) to generate synthetic populations conditional on complex sample design data that can be treated as simple random samples at the imputation stage, obviating the need to directly model design features for imputation. We develop two forms of this method: one where probabilities of selection are known at the first and second stage of the design, and the other, where only the final weight based on the product of the two probabilities are known. We show via simulation study this method has advantages in terms of bias, mean square error, and coverage properties over methods where sample designs are ignored, with little loss in efficiency even when compared with correct fully parametric models.