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chapter 1. An Introduction to Missing Data  chapter  2. Traditional Methods for Dealing with Missing Data  3. An Introduction to Maximum Likelihood Estimation 4. Maximum Likelihood Missing Data Handling 5. Improving the Accuracy of Maximum Likelihood Analyses 6. An Introduction to Bayesian Estimation 7. The Imputation Phase of Multiple Imputation 8. The Analysis and Pooling Phases of Multiple Imputation 9. Practical Issues in Multiple Imputation 10. Models for Missing Not at Random Data 11. Wrapping Things Up: Some Final Practical Considerations