The MI procedure performs multiple imputation of missing data. Missing values are an issue in a substantial number of statistical analyses. Most SAS statistical procedures exclude observations with any missing variable values from the analysis. These observations are called incomplete cases. Although analyzing only complete cases has the advantage of simplicity, the information contained in the incomplete cases is lost. This approach also ignores possible systematic differences between the complete cases and the incomplete cases, and the resulting inference might not be applicable to the population of all cases, especially with a small number of complete cases.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Shoukri, Mohamed M.: Analysis of correlated data with SAS and R (2018)
- Kombo, A. Y.; Mwambi, H.; Molenberghs, G.: Multiple imputation for ordinal longitudinal data with monotone missing data patterns (2017)
- Mitani, Aya A.; Kurian, Allison W.; Das, Amar K.; Desai, Manisha: Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects (2015)
- Yang Yuan: Multiple Imputation Using SAS Software (2011) not zbMATH
- Horton, Nicholas J.; Lipsitz, Stuart R.; Parzen, Michael: A potential for bias when rounding in multiple imputation (2003)