Multiple Imputation Methods in SOLAS
- Mahalanobis Distance Metric Matching
- Predictive Mean Matching Method
- Predictive Model Based Multiple Imputation
- Propensity Score Based Mulitple Imputation
- Propensity Score / Predictive Mean / Mahalanobis Distance Combination Method
Major Advantages of Multiple Imputation to Impute Missing Data:
- Better statistical validity than ad-hoc approaches
- Multiple Imputation is statistically efficient in that it uses the entire observed dataset in the statistical analysis, efficiency being the degree to which all information about the parameter of interest, available in the dataset, is used.
- Multiple Imputation saves money, since for the same statistical power, multiple imputation requires a smaller sample size than listwise deletion
- Once imputations have been generated by a knowledgeable user, researchers can use them for their own statistical analyses.