Series Editor′s Introduction 1. Introduction 2. Assumptions Missing Completely at Random Missing at Random Ignorable Nonignorable 3. Conventional Methods Listwise Deletion Pairwise Deletion Dummy Variable Adjustment Imputation Summary 4. Maximum Likelihood Review of Maximum Likelihood ML With Missing Data Contingency Table Data Linear Models With Normally Distributed Data The EM Algorithm EM Example Direct ML Direct ML Example Conclusion 5. Multiple Imputation: Bascis Single Random Imputation Multiple Random Imputation Allowing for Random Variation in the Parameter Estimates Multiple Imputation Under the Multivariate Normal Model Data Augmentation for the Multivariate Normal Model Convergence in Data Augmentation Sequential Verses Parallel Chains of Data Augmentation Using the Normal Model for Nonnormal or Categorical Data Exploratory Analysis MI Example 1 6. Multiple Imputation: Complications Interactions and Nonlinearities in MI Compatibility of the Imputation Model and the Analysis Model Role of the Dependent Variable in Imputation Using Additional Variables in the Imputation Process Other Parametric Approaches to Multiple Imputation Nonparametric and Partially Parametric Methods Sequential Generalized Regression Models Linear Hypothesis Tests and Likelihood Ratio Tests MI Example 2 MI for Longitudinal and Other Clustered Data MI Example 3 7. Nonignorable Missing Data Two Classes of Models Heckman′s Model for Sample Selection Bias ML Estimation With Pattern-Mixture Models Multiple Imputation With Pattern-Mixture Models 8. Summary and Conclusion Notes References About the Author