Simple methods to handle missing data

Missing data are a common problem in big data sets. Specifically, missing data are present in surveys and in different studies, leading to increase of variance and unreliable results. While most of the researchers focus on the analysis of more sophisticated methods, the simplest techniques are not treated in detail. The article explains the theoretical concepts of different types of missing data, the causes of missing data, and analyses methods on how to deal with missing data. The focus is using simple imputation techniques (mean imputation, regression imputation and non-treating missing at all). The analysis is done using Malawi data, IHS5 2019-2020 survey data. In this article, the interest is to know the whole property values (selling and renting) in the country, while the information in these variables is partly not filled. The results show how the different imputation methods influence the results and sometimes the value is predicted from other auxiliary variables.