Applied missing data analysis - Enders, Craig K. (New York : The Guilford Press, 2022)
"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"--
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.
Revised edition of the author's Applied missing data analysis, 2010.
Includes bibliographical references (pages 493-517) and index.
Keywords: Social sciences -- Statistical methods; Missing observations (Statistics); Social sciences -- Research -- Methodology