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Applied Missing Data Analysis (Methodology in the Social Sciences)

4.1  ·  Rating Details ·  10 Ratings  ·  2 Reviews

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 n

Hardcover, 382 pages
Published April 23rd 2010 by The Guilford Press
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Oct 03, 2012 Elle rated it it was amazing
I've been reading a bunch of journal articles about missing data and imputation, but had I known this book would be so clear, concise and thorough, I wouldn't have bothered with the journal articles. I've found a lot of the terminology (types of missing data mechanisms, types of strategies) confusing and not well defined in the literature; this book helped me understand the terminology not only through better descriptions, definitions and examples of the terms, but through Dr. Enders' work in ...more
First two chapters are excellent for conceptually understanding the effects that missing data has on inferences and the various ways missing data has been handled in the past (and why none of those are ideal). I regularly assign these to my students, and they always get a lot out of them.

The remainder of the book explains the multiple imputation method in detail.
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Other Books in the Series

Methodology in the Social Sciences (1 - 10 of 14 books)
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  • Confirmatory Factor Analysis for Applied Research, First Edition
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  • Missing Data: A Gentle Introduction
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  • A Primer on Regression Artifacts
  • Principles and Practice of Structural Equation Modeling (Methodology In The Social Sciences)
  • Regression Analysis for Categorical Moderators

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