e-book Missing Data: Analysis and Design (Statistics for Social and Behavioral Sciences)

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Enders provides a much-needed overview and explication of the current technical literature on missing data.

Applied Missing Data Analysis

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.

Missing Data - Analysis and Design | John W. Graham | Springer

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. 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.


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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.

About the Author

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.

Lecture slides.

Missing data analysis

Example multiple imputation do-file. Replication files.

Longitudinal Modeling with Modern Treatments for Missing Data

Very little data of interest to social, behavioral, and health scientists has complete information. Instead, some missing data tends to be the rule rather than the exception in applied data analysis. Survey respondents may choose to skip sensitive questions; economic data may be harder to find for developing countries; certain types of respondents may be most likely to drop out of panel studies.