This handbook presents many methodological advances and the latest applications of missing data methods in empirical research. It outlines a general taxonomy of missing data mechanisms and their implications for analysis and describes alternatives for estimating models when data are missing. The book covers a range of approaches that assess the sensitivity of inferences to alternative assumptions about the missing data process. It also discusses how to handle missing data in clinical trials and sample surveys.
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