Roles of imputation methods for filling the missing values: A review

Abstract

Missing data are often encountered in many areas of research. Complete case analysis and indicator method can lead to serious bias. One of the comforting methods is implementation of imputation methods. The main purpose of this paper is to review the agreement of imputation methods as the most widely used method for filling missing observations. Single and multiple imputations had certain criteria to be satisfied before adoption. Single imputation methods works excellently in short gap length of missing data. Embracing single imputation method to the long gap of missing data will cause systematically error since the reflection of uncertainty is not covered. Multiple imputations were recognized as the superior method for missing-at-random (MAR) data set. Although the dominance of multiple imputations was known, the adoption of these imputations needs thorough understanding on the algorithms especially in designing a suitable method to perform the imputations. The reviews on assessment of available imputation software were also presented to compare the practicality of the software.