A survey of methodologies for the treatment of missing values within datasets: limitations and benefits

Knowledge discovery in ergonomics is complicated by the presence of missing data, because most methodologies do not tolerate incomplete sample instances. Data-miners cannot always remove sample instances when they occur. Imputation methods are needed to 'fill in' estimated values for the missing instances in order to construct a complete dataset. Even with emerging methodologies, the ergonomics field seems to rely on outdated imputation techniques. This survey presents an overview of a variety of imputation methods found in current academic research, which is not limited to ergonomic studies. The objective is to strengthen the communities' understanding of imputation methodologies and briefly highlight their benefits and limitations. This survey suggests that the multiple imputation method is the current state-of-the-art missing value technique. This method has proven to be robust to many of the shortcomings that plague other methods and should be considered the primary choice for missing value problems found in ergonomic studies.