Diagnostics for Multivariate Imputations∗

We consider three sorts of diagnostics for random imputations: (a) displays of the completed data, intended to reveal unusual patterns that might suggest problems with the imputations, (b) comparisons of the distributions of observed and imputed data values, and (c) checks of the fit of observed data to the model used to create the imputations. We formulate these methods in terms of sequential regression multivariate imputation [Van Buuren and Oudshoom 2000, and Raghunathan, Van Hoewyk, and Solenberger 2001], an iterative procedure in which the missing values of each variable are randomly imputed conditional on all the other variables in the completed data matrix. We also consider a recalibration procedure for sequential regression imputations. We apply these methods to the 2002 Environmental Sustainability Index (ESI), a linear aggregation of 68 environmental variables on 142 countries, with 22% missing values.