Applied Missing Data Analysis Methodology In The Social Sciences


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ISBN: 4281393346
Number of pages: 280
Language: English (Original)
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  1. Applied Missing Data Analysis : Craig K. Enders :
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Applied Missing Data Analysis : Craig K. Enders :

Dispatched from the UK in 1 business day When will my order arrive? Andrew F. Rex B. Christian Geiser. Todd D. Niall Bolger. James Jaccard.

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.

Product details Format Hardback pages Dimensions x x Other books in this series. Add to basket. Data Analysis with Mplus Christian Geiser. Intensive Longitudinal Methods Niall Bolger. Dyadic Data Analysis David A. Psychometric Methods Larry Price. Missing Data Patrick E. Growth Modeling Kevin J. Table of contents 1.

By using our website you agree to our use of cookies. Rex B. We use cookies to give you the best possible experience. Dispatched from the UK in 1 business day When will my order arrive? Andrew F.

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.

Bestselling Series

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.

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  • Applied Missing Data Analysis Methodology In The Social Sciences.

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  • You are also expected to complete independent study outside of class time.

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.

Applied Missing Data Analysis Methodology in the Social Sciences video dailymotion

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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victorss This interdisciplinary programme will provide you with training in fundamental aspects of applied data science, computation and programming, and quantitative methods. With the rise of new and big forms of data, and computation and analytics forming ever-increasingly important elements of a wide range of professions, this programme with prepare you for a variety of careers in the private, non-profit and public sectors.
yanetskaya We are pleased to offer extensive sets of downloadable files and online resources to accompany several titles from Guilford's Methodology in the Social Sciences Series. This series provides applied researchers and students with analysis and research design books that emphasize the use of methods to answer research questions. Rather than emphasizing statistical theory, each volume in the series illustrates when a technique should and should not be used and how the output from available software programs should and should not be interpreted. Common pitfalls as well as areas of further development are clearly articulated.
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samjenkins 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.