  Resampling techniques have become increasingly popular for estimation of
uncertainty in data collected via surveys. Survey data are also frequently
subject to missing data which are often imputed. This note addresses the issue
of using resampling methods such as a jackknife or bootstrap in conjunction
with imputations that have be sampled stochastically (e.g., in the vein of
multiple imputation). It is illustrated that the imputations must be redrawn
within each replicate group of a jackknife or bootstrap. Further, 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 brief simulation study is provided to support the theory
introduced in this note.
