The problem of missing data is of great practical and theoretical interest in statistics. Applications of Modern Missing Data Methods examines case studies, data analysis, and methods enabled to handle such problems. The book covers several key examples including historically important case studies in which specialized software was used, recent examples using specialized software, and recent examples using generally available software. The introduction of the book provides the necessary technical background to understand the subsequent examples. Written by recognized experts who have contributed extensively to the literature, this book provides the necessary tools to deal with the pervasive problem of missing data.