multiple imputation

multiple imputation?

 it is a statistical technique for analyzing incomplete data sets, that is, data sets for which some entries are missing. Application of the technique requires three steps: imputation, analysis and pooling. The figure illustrates these steps.

1. Imputation: Impute (=fill in) the missing entries of the incomplete data sets, not once, but m times (m=3 in the figure). Imputed values are drawn for a distribution (that can be different for each missing entry). This step results is m complete data sets. 
2. Analysis: Analyze each of the m completed data sets. This step results in m analyses.
3. Pooling: Integrate the m analysis results into a final result. Simple rules exist for combining the m analyses.