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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.
Introduction to Missing Data
Maximum Likelihood Estimation
Maximum Likelihood Estimation with Missing Data
Bayesian Estimation
Bayesian Estimation with Missing Data
Bayesian Estimation for Categorical Variables
Multiple Imputation
Multilevel Missing Data
Missing Not at Random Processes
Special Topics and Applications
Wrap‑Up