Applied Missing Data Analysis, Second Edition (Hardcover)

Craig K. Enders

ISBN 10: 1462549861 / ISBN 13: 9781462549863

Published by Guilford Publications, New York, 2022

About this Item

Hardcover. 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 data sets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition *Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. *New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new. Revised edition of the author's Applied missing data analysis, c2010. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781462549863

Bibliographic Details

• Title: Applied Missing Data Analysis, Second Edition (Hardcover)
• Publisher: Guilford Publications, New York
• Publication Date: 2022
• Language: English
• ISBN 10: 1462549861
• ISBN 13: 9781462549863
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: new

About this title

Synopsis

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 (www.appliedmissingdata.com) includes data sets and analysis examples from the book, up-to-date software information, and other resources.
 
New to This Edition
*Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables.
*New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics.
*Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples.
*Most of the book is entirely new.

About the Author

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.