The book "Applied Missing Data Analysis" by Craig K. Enders provides a comprehensive guide to handling missing data in statistical analysis. It begins with an introduction to missing data patterns and theories, emphasizing the importance of understanding the missing data mechanism. The book then covers traditional methods for dealing with missing data, such as deletion and imputation techniques, and introduces maximum likelihood estimation and Bayesian estimation. It delves into the practical aspects of multiple imputation, including the imputation phase, analysis, and pooling phases, and addresses common issues like convergence problems and non-normal data. The book also explores models for missing not at random (MNAR) data, providing theoretical and practical insights. Finally, it offers practical considerations for software options and reporting results from missing data analyses. The companion website provides additional resources, including data files and software information.The book "Applied Missing Data Analysis" by Craig K. Enders provides a comprehensive guide to handling missing data in statistical analysis. It begins with an introduction to missing data patterns and theories, emphasizing the importance of understanding the missing data mechanism. The book then covers traditional methods for dealing with missing data, such as deletion and imputation techniques, and introduces maximum likelihood estimation and Bayesian estimation. It delves into the practical aspects of multiple imputation, including the imputation phase, analysis, and pooling phases, and addresses common issues like convergence problems and non-normal data. The book also explores models for missing not at random (MNAR) data, providing theoretical and practical insights. Finally, it offers practical considerations for software options and reporting results from missing data analyses. The companion website provides additional resources, including data files and software information.