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With advances in computing power, there have been substantial developments in computational methods for handling missing data. This text presents an introduction to the theory, applications, and computational aspects of missing data analysis. It covers the three main methodological approaches: likelihood-based, nonparametric, and quasi-randomization. The text includes many real examples and integrates computer code where appropriate. It also provides exercises at the end of each chapter. A solutions manual is available for qualifying instructors.