A Systematic Literature Review On Missing Values: Research Trends, Datasets, Methods and Frameworks

Abstract

Handling of missing values in data analysis is the focus of attention in various research fields. Imputation is one method that is commonly used to overcome this problem of missing data. This systematic literature review research aims to present a comprehensive summary of the relevant scientific literature that describes the use of the imputation method in overcoming missing values. The literature search method is carried out using various academic databases and reliable sources of information. Relevant keywords are used to find articles that match the research question. After selection and evaluation, 40 relevant articles were included in this study. The findings of this study reveal a variety of imputation approaches and methods used in various research fields, such as social sciences, medicine, economics, and others. Commonly used imputation methods include single imputation, multivariate imputation, and model-based imputation methods. In addition, several studies also describe a combination of imputation methods to deal with more complex situations. The advantage of the imputation method is that it allows researchers to maintain sample sizes and minimize bias in data analysis. However, the research results also show that the imputation method must be applied with caution, because inappropriate imputation decisions can lead to biased results and can affect the accuracy of the research conclusions. In order to increase the validity and reliability of research results, researchers are expected to transparently report the imputation method used and describe the considerations made in the imputation decision-making process. This systematic review of the literature review provides an in-depth view of the use of the imputation method in handling missing values. In the face of the challenge of missing data, an understanding of the various imputation methods and the context in which they are applied will be key to generating meaningful findings in various research fields.