What if the imputation model is wrong?
Experienced analysts know that real data rarely conform to convenient models such as the multivariate normal. In most applications of MI, the model used to generate the imputations will at best be only approximately true. Fortunately, experience has repeatedly shown that MI tends to be quite forgiving of departures from the imputation model. For example, when working with binary or ordered categorical variables, it is often acceptable to impute under a normality assumption and then round off the continuous imputed values to the nearest category. Variables whose distributions are heavily skewed may be transformed (e.g. by taking logarithms) to approximate normality and then transformed back to their original scale after imputation.
