|Number of pages:||280|
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May 4, This book is a title in the series 'Methodology in the Social Sciences', and a stated aim of the book is 'to “translate” the technical missing data.
Paul E. Timothy A. David A. Craig K. Richard B. Larry Price. David Kaplan. Patrick E. Noel A. Kevin J. Deborah L. Christopher Mccarty. Home Contact us Help Free delivery worldwide. Free delivery worldwide. Bestselling Series. Harry Potter. Popular Features. New Releases. Applied Missing Data Analysis. Description 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. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.
An Introduction to Missing Data 1. Traditional Methods for Dealing with Missing Data 2. An Introduction to Maximum Likelihood Estimation 3. Maximum Likelihood Missing Data Handling 4. Improving the Accuracy of Maximum Likelihood Analyses 5. An Introduction to Bayesian Estimation 6. The Impu show more. Review quote "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. 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.
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. I have no doubt that this book will serve as a solid reference for quantitative social and behavioral scientists. 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. I would recommend it to colleagues and students, especially those who do not have formal training in mathematical statistics.
The simulations are excellent and are a clear strength of the book. About Craig K. Enders Craig K. 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.
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