Abstract
In many areas (e.g. sample survey, medical research, industrial experiment, etc.), researchers often encounter incomplete data for various reasons. Therefore, when designing a study, researchers must consider how to handle the incomplete data that might occur in their study. Recently the availability of software which provides statistical analysis for incomplete data has been increasing. In this paper we compare the functions and the characteristics of six software packages for statistical analyses for incomplete data and make a proposal on how to choose the software package depending on the purposes. With regard to the statistical methods, the main focus is on the EM algorithm (Dempster et al., 1977) and multiple imputation (Rubin, 1987). Finally, using randomly generated data, the output of some software packages are compared and also the differences in the results between statistical analysis methods for incomplete data are examined.
