Includes bibliographical references (pages 347-358) and indexes.
An introduction to missing data -- Traditional methods for dealing with missing data -- An introduction to maximum likelihood estimation -- Maximum likelihood missing data handling -- Improving the accuracy of maximum likelihood analyses -- An introduction to Bayesian estimation -- The imputation phase of multiple imputation -- The analysis and pooling phases of multiple Imputation -- Practical issues in multiple imputation -- Models for missing not at random data -- Wrapping things up : some final practical considerations.
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"Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists"--Back cover.
Missing observations (Statistics)
Social sciences-- Research-- Methodology.
Social sciences-- Statistical methods.
Social Sciences-- statistics & numerical data [MESH].
70.03 methods, techniques and organization of social science research.