Multiple Imputation for Missing Data: Concepts and New ...

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Multiple Imputation for Missing Data: Concepts and NewDevelopment (Version )Yang C. Yuan, SAS Institute Inc., Rockville, MDAbstractMultiple Imputation provides a useful strategy for dealingwith data sets with Missing values. Instead of filling in asingle value for each Missing value, Rubin s (1987) multipleimputation procedure replaces each Missing value with aset of plausible values that represent the uncertainty aboutthe right value to impute. These multiply imputed data setsare then analyzed by using standard procedures for com-plete data and combining the results from these matter which complete-data analysis is used, the pro-cess of combining results from different imputed data setsis essentially the same.

the uncertainty about the right value to impute. The multiply imputed data sets are then analyzed by using standard pro-cedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different data sets is essentially the same.

  Analysis, Multiple, Results, Combining, Imputation, Multiply, Multiple imputation, Combining results