The process of multiple imputation involves three main steps:
Imputation: Generate multiple (e.g., 5-10) complete datasets by filling in the missing values with plausible data points based on the observed data. Analysis: Perform the desired statistical analysis separately on each of the imputed datasets. Pooling: Combine the results from each analysis to produce overall estimates and confidence intervals that reflect the uncertainty due to missing data.
