In multiple imputation (MI), each missing value is replaced by a set of m > 1 plausible values drawn from their predictive distribution. hi multiply imputed dataset is depicted in Figure 1. The variation among the m
imputations reflects the uncertainty with which the missing values can be predicted from the observed ones. After performing MI there are m apparently complete datasets, each of which can be analyzed by completedata methods. After performing identical analyses on each of the m datasets, the results (estimates and standard errors) are combined, using simple rules provided by Rubin (1987) and others, to produce overall estimates and standard errors thal reflect missing-data uncertainty.