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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 (www.appliedmissingdata.com) 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.
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Craig K. Enders is Associate Professor in the Quantitative Psychology concentration in the Department of Psychology at Arizona State University. The majority of his research focuses on analytic issues related to missing data analyses. He also does research in the area of structural equation modeling and multilevel modeling. Dr. Enders is a member of the American Psychological Association and is also active in the American Educational Research Association.Review:
"This is a well-written book that will be particularly useful for analysts who are not PhD statisticians. Enders provides a much-needed overview and explication of the current technical literature on missing data. The book should become a popular text for applied methodologists."--Bengt Muthén, PhD, Professor Emeritus, University of California, Los Angeles "Many applied researchers are not trained in statistics to the level that would make the classic sources on missing data accessible. Enders makes a concerted--and successful--attempt to convey the statistical concepts and models that define missing data methods in a way that does not assume high statistical literacy. He writes in a conceptually clear manner, often using a simple example or simulation to show how an equation or procedure works. This book is a refreshing addition to the literature for applied social researchers and graduate students doing quantitative data analysis. It covers the full range of state-of-the-art methods of handling missing data in a clear and accessible manner, making it an excellent supplement or text for a graduate course on advanced, but widely used, statistical methods."--David R. Johnson, PhD, Department of Sociology, The Pennsylvania State University "A useful overview of missing data issues, with practical guidelines for making decisions about real-world data. This book is all about an issue that is usually ignored in work on OLS regression--but that most of us spend significant time dealing with. The writing is clear and accessible, a great success for a challenging topic. Enders provides useful reminders of what we need to know and why. I appreciated the interpretation of formulas, terms, and output. This book provides comprehensive and vital information in an easy-to-consume style. I learned a great deal reading it."--Julia McQuillan, PhD, Director, Bureau of Sociological Research, and Department of Sociology, University of Nebraska-Lincoln "I would certainly recommend this book to anybody who deals with missing data at any level. I have no doubt that this book will serve as a solid reference for quantitative social and behavioral scientists."--Hakan Demirtas, PhD, Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago "The chapter on MNAR provides a good overview of the current state of the art. I would recommend it to anyone working with missing data, as well as to developers of multilevel and structural equation modeling software who are interested in adding new features, such as pattern mixture models. The focus is on the 'how-tos' of working with MNAR data. The author illustrates the many pitfalls and how different model assumptions could lead to different parameter estimates and standard error estimates, and hence to different conclusions."--Stephen du Toit, PhD, Senior VP of Technical Operations, Scientific Software International, Inc. "I would highly recommend this book to colleagues and will require it in my advanced graduate courses on longitudinal data analysis."--Scott M. Hofer, PhD, Professor and Mohr Chair in Adult Development and Aging, Department of Psychology, University of Victoria, Canada "The book contains very accessible material on missing data. I would recommend it to colleagues and students, especially those who do not have formal training in mathematical statistics."--Ke-Hai Yuan, PhD, Department of Psychology, University of Notre Dame "A needed and valuable addition to the literature on missing data. The simulations are excellent and are a clear strength of the book."--Alan C. Acock, PhD, Distinguished Professor and Knudson Chair in Family Research, Department of Human Development and Family Sciences, Oregon State University
"The book is well written, and successfully achieves the goal, stated in the Preface, of 'translat[ing] the technical missing data literature into an accessible reference text' (p. vii) for the social sciences. The author successfully achieved the goal of helping the reader to become familiar with basic concepts in missing data analysis procedures, and to feel comfortable using these procedures in a variety of practical and social science applications. It contains very useful examples and illustrations in the applied social sciences. In addition, those example and illustration datasets and detailed software implementations are available on the book's website http://www.appliedmissingdata.com, which is invaluable." (American Statistician 2011-08-03)
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