This book, "Applied Missing Data Analysis" by Craig K. Enders, provides a comprehensive overview of methods for handling missing data in statistical analysis. The book is structured into ten chapters, each focusing on different aspects of missing data analysis. The first chapter introduces the concept of missing data, discusses various missing data patterns, and explains the importance of the missing data mechanism. It also covers an inclusive analysis strategy and the testing of the missing completely at random (MCAR) mechanism. The second chapter discusses traditional methods for dealing with missing data, including deletion methods and single imputation techniques. The third chapter introduces maximum likelihood estimation, explaining the univariate normal distribution, the sample likelihood, and the role of first derivatives in parameter estimation. It also covers the use of the Wald and likelihood ratio tests. The fourth chapter focuses on maximum likelihood missing data handling, detailing the EM algorithm and its application to multivariate data. The fifth chapter improves the accuracy of maximum likelihood analyses by incorporating auxiliary variables and using robust standard errors. The sixth chapter introduces Bayesian estimation, explaining Bayes' theorem and its application to multiple imputation. The seventh and eighth chapters discuss the imputation and analysis phases of multiple imputation, including convergence diagnostics and pooling methods. The ninth chapter addresses practical issues in multiple imputation, such as convergence problems and non-normal data. The tenth chapter covers models for missing not at random (MNAR) data, including selection models, pattern mixture models, and longitudinal growth models. The book also includes data analysis examples, recommended readings, and references, making it a valuable resource for researchers and practitioners dealing with missing data in their analyses.This book, "Applied Missing Data Analysis" by Craig K. Enders, provides a comprehensive overview of methods for handling missing data in statistical analysis. The book is structured into ten chapters, each focusing on different aspects of missing data analysis. The first chapter introduces the concept of missing data, discusses various missing data patterns, and explains the importance of the missing data mechanism. It also covers an inclusive analysis strategy and the testing of the missing completely at random (MCAR) mechanism. The second chapter discusses traditional methods for dealing with missing data, including deletion methods and single imputation techniques. The third chapter introduces maximum likelihood estimation, explaining the univariate normal distribution, the sample likelihood, and the role of first derivatives in parameter estimation. It also covers the use of the Wald and likelihood ratio tests. The fourth chapter focuses on maximum likelihood missing data handling, detailing the EM algorithm and its application to multivariate data. The fifth chapter improves the accuracy of maximum likelihood analyses by incorporating auxiliary variables and using robust standard errors. The sixth chapter introduces Bayesian estimation, explaining Bayes' theorem and its application to multiple imputation. The seventh and eighth chapters discuss the imputation and analysis phases of multiple imputation, including convergence diagnostics and pooling methods. The ninth chapter addresses practical issues in multiple imputation, such as convergence problems and non-normal data. The tenth chapter covers models for missing not at random (MNAR) data, including selection models, pattern mixture models, and longitudinal growth models. The book also includes data analysis examples, recommended readings, and references, making it a valuable resource for researchers and practitioners dealing with missing data in their analyses.