The process of multiple imputation involves three main steps:
Imputation: Multiple sets (usually around 5-10) of plausible values are generated for the missing data based on the observed data. This process creates several complete datasets. Analysis: Each of these datasets is analyzed separately using standard statistical methods. Pooling: The results from each of these analyses are combined to produce a single set of estimates and confidence intervals. This pooling accounts for the uncertainty introduced by the missing data.
