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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 is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation, Bayesian estimation, and multiple imputation. The…mehr

Produktbeschreibung
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 is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation, Bayesian estimation, and multiple imputation. The companion website includes datasets and analysis examples from the book, plus other resources.
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Autorenporträt
Craig K. Enders, PhD, is Professor and Area Chair in Quantitative Psychology in the Department of Psychology at the University of California, Los Angeles. His primary research focus is on analytic issues related to missing data analyses, and he leads the research team responsible for developing the Blimp software application for missing data analyses. Dr. Enders also conducts research in the areas of multilevel modeling and structural equation modeling, and is an active member of the Society of Multivariate Experimental Psychology, the American Psychological Association, and the American Educational Research Association.