Log-linear models for contingency tables with missing data

Missing value is the absence of a value in one or more cells in the data set. Missing values are frequently encountered in areas such as health, ecology, economics, and social sciences where it is difficult to obtain data. If there are missing values in the data, incorrect results may emerge in the statistical analysis. For this reason, the missing value problem should be solved before data analysis. Within the scope of this thesis, it is aimed to examine the types of missing values and the methods of dealing with missing values, to introduce the methods used for contingency tables and to introduce log-linear models and discuss these methods through application.