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"The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website includes datasets and analysis examples from the book, up-to-date software information, and other resources. Subject areas/Key words: advanced quantitative methods, management, survey, longitudinal, structural equation modeling, handling, how to handle, incomplete, multivariate, social research, behavioral sciences, statistical techniques, textbooks, seminars, doctoral courses, multiple imputation, models, MCAR, MNAR, Bayesian Audience: Researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science."--

Cover -- Half Title Page -- Series Editor -- Title Page -- Copyright -- Series Editor's 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.