Applied missing data analysis Cover Half Title Page Series Editor Title Page Copyright Series Editors Note Preface Contents 1. Introduction to Missing Data 1.1 Chapter Overview 1.2 Missing Data Patterns 1.3 Missing Data Mechanisms 1.4 Diagnosing Missing Data Mechanisms 1.5 Auxiliary Variables 1.6 Analysis Example: Preparing for Missing Data Handling 1.7 Older Missing Data Methods 1.8 Comparing Missing Data Methods via Simulation 1.9 Planned Missing Data 1.10 Power Analyses for Planned Missingness Designs 1.11 Summary and Recommended Readings 2. Maximum Likelihood Estimation 2.1 Chapter Overview 2.2 Probability Distributions versus Likelihood Functions 2.3 The Univariate Normal Distribution 2.4 Estimating Unknown Parameters 2.5 Getting an Analytic Solution 2.6 Estimating Standard Errors 2.7 Information Matrix and Parameter Covariance Matrix 2.8 Alternative Approaches to Estimating Standard Errors 2.9 Iterative Optimization Algorithms 2.10 Linear Regression 2.11 Significance Tests 2.12 Multivariate Normal Data 2.13 Categorical Outcomes: Logistic and Probit Regression 2.14 Summary and Recommended Readings 3. Maximum Likelihood Estimation with Missing Data 3.1 Chapter Overview 3.2 The Multivariate Normal Distribution Revisited 3.3 How Do Incomplete Data Records Help 3.4 Standard Errors with Incomplete Data 3.5 The Expectation Maximization Algorithm 3.6 Linear Regression 3.7 Significance Testing 3.8 Interaction Effects 3.9 Curvilinear Effects 3.10 Auxiliary Variables 3.11 Categorical Outcomes 3.12 Summary and Recommended Readings 4. Bayesian Estimation 4.1 Chapter Overview 4.2 What Makes Bayesian Statistics Different 4.3 Conceptual Overview of Bayesian Estimation 4.4 Bayes Theorem 4.5 The Univariate Normal Distribution.