Part I: Overview and historical approaches — 1. Introduction — 2. Missing data in experiments — 3. Quick methods for multivariate data with missing values — 4. Nonresponse in sample surveys — Part II: Likelihood-based approaches to the analysis of missing data — 5. Theory of inference based on the likelihood function — 6. Methods based on factoring the likelihood, ignoring the missing -data mechanism — 7. Maximum likelihood for general patterns of missing data: introduction and theory with ignorable nonresponse — 8. Maximum likelihood estimation for multivariate normal examples, ignoring the missing-data mechanism — 9. Models for partially classified contingency tables, ignoring the missing-data mechanism — 10. Mixed normal and nonnormal data with missing values, ignoring the missing-data mechanism — 11. Nonignorable missing-data models — 12. The model-based approach to survey nonresponse.