Missing Data Analysis in Practice

This book provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. The author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online. The book also includes practical examples, simulation studies, projects, and end-of-chapter exercises.