Partitioned predictive mean matching as a large data multilevel imputation technique.

Large scale assessment data often has a multilevel structure. When dealing with missing values,
such structures need to be taken into account to prevent underestimation of the intraclass correlation.
We evaluate predictive mean matching (PMM) as a multilevel imputation technique and compare
it to other imputation approaches for multilevel data. We propose partitioned predictive mean
matching (PPMM) as an extension to the PMM algorithm to divide the big data multilevel
problem into manageable parts that can be solved by standard predictive mean matching. We
show that PPMM can be a very effective imputation approach for large multilevel datasets and
that both PPMM and PMM yield plausible inference for continuous, ordered categorical, or even
dichotomous multilevel data. We conclude that both the performance of PMM and PPMM is often
comparable to dedicated methods for multilevel data.