Resampling methods with multiply imputed data

Music City Center 

Resampling techniques have become increasingly popular for estimation of uncertainty in a wide range of data, including those collected via surveys. Such data are often fraught with missing values which are commonly imputed to facilitate analysis. This article addresses the issue of using resampling methods such as a jackknife or bootstrap in conjunction with imputations that have been sampled stochastically (e.g., in the vein of multiple imputation). We derive the theory needed to illustrate two key points. First, imputations must be independently generated multiple times within each replicate group of a jackknife or bootstrap. Second, the number of multiply imputed datasets per replicate group must dramatically exceed the number of replicate groups for a jackknife; however, this is not the case in a bootstrap approach. A simulation study is provided to support these theoretical conclusions.

Missing data

Multiple imputation

Jackknife

Bootstrap 

Survey Research Methods Section