The Performance of Multiple Imputations for Different Number of Imputations

Abstract

Multiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step in the first stage is important. Hence, this study aimed to examine the performance of multiple imputation method at different numbers of imputations. Monotone missing data pattern was created in the study by deleting approximately 24% of the observations from the continuous result variable with complete data. At the first stage of the multiple imputation method, monotone regression imputation at different numbers of imputations (m=3, 5, 10 and 50) was performed. In the second stage, parameter estimations and their standard errors were obtained by applying general linear model to each of the complete data sets obtained. In the final stage, the obtained results were pooled and the effect of the numbers of imputations on parameter estimations and their standard errors were evaluated on the basis of these results. In conclusion, efficiency of parameter estimations at the number of imputation m=50 was determined as about 99%. Hence, at the determined missing observation rate, increase was determined in efficiency and performance of the multiple imputation method as the number of imputations increased.