The process of multiple imputation typically involves three steps:
Imputation: Generate multiple datasets where the missing values are replaced with plausible estimates. This is usually done using models that predict missing values based on observed data. Analysis: Each dataset is analyzed separately using the same statistical methods. Pooling: The results from each dataset are combined to produce a single set of estimates and standard errors, accounting for the variability between imputations.
