Desktop Survival Guide
by Graham Williams
Multiple imputation (MI) is a general purpose method for handling of missing data. The basic idea is: Impute missing values using an appropriate model that incorporates random variation; Do this times (often 3-5 times) to obtain datasets, all with no missing values; Do the intended analysis on each of these datasets; Gert the average values of the parameter estimates across the samples to have a single point estimate; Calculate standard errors by firstly averaging the squared standard errors of the estimates and calculating the variance of the parameter estimates across samples, and then combine these in some way.
There are a number of R packages for imputation.