Missing Data (Quantitative Applications in the Social Sciences) [Paperback]

Paul D. Allison (Author)

Book Description

Publication Date: August 13, 2001 | ISBN-10: 0761916725 | ISBN-13: 978-0761916727 | Edition: 1

Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data. 

 

Editorial Reviews

Review

"…an excellent resource for researchers who are conducting multivariate statistical studies."

(Richard A. Chechile )

About the Author

Paul D. Allison is Professor of Sociology at the University of Pennsylvania, where he teaches advanced graduate courses on event history analysis, categorical data analysis, and structural equation models with latent variables. He is the author of seven books and more than 50 journal articles. Every summer he teaches 5-day workshops on survival analysis and logistic regression analysis that draw about 100 researchers from around the U.S.  A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology.

Product Details

• Paperback: 104 pages
• Publisher: Sage Publications, Inc; 1 edition (August 13, 2001)
• Language: English
• ISBN-10: 0761916725
• ISBN-13: 978-0761916727
• Product Dimensions: 8.5 x 5.4 x 0.3 inches
• Shipping Weight: 4.5 ounces (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (6 customer reviews)
• Amazon Best Sellers Rank: #64,677 in Books (See Top 100 in Books)

More About the Author

Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania, and President of Statistical Horizons LLC. You can visit his website at www.StatisticalHorizons.com. After completing doctoral work in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logit analysis, probit analysis, measurement error, inequality measures, missing data, Markov processes, and event history analysis. Much of his earlier research was focused on career patterns of academic scientists. At present, his principal methodological research is focused on the analysis of longitudinal data, especially with determining the causes and consequences of events, and on methods for handling missing data. Each summer he teaches 5-day workshops on survival analysis and logistic regression analysis that draw about 90 doctorate-level researchers from around the U.S. At Penn, he teaches advanced graduate courses on event history analysis, categorical data analysis, and structural equation models with latent variables. A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010, he was elected as a Fellow of the American Statistical Association.

Customer Reviews

Most Helpful Customer Reviews

33 of 33 people found the following review helpful:

5.0 out of 5 stars nice coverage of the realities of missing data, May 30, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Missing Data (Quantitative Applications in the Social Sciences) (Paperback)

I echo wired weird's comments about this monograph. Allison has written some very useful applied statistics books that often include instructions for implimenting the methods in SAS. He writes very well. The series of Sage monographs is usually of high quality, informative and concise and this one clearly fits that mold. These little and inexpensive paperback monographs are also good reference guides. You can't find anything better for under $20.

23 of 23 people found the following review helpful:

5.0 out of 5 stars Fabulous primer on handling missing data, October 17, 2001

By 

James Hinterlong (St. Louis, MO USA) - See all my reviews

This review is from: Missing Data (Quantitative Applications in the Social Sciences) (Paperback)

As usual, Paul Allison has produced an accessible and practical treatment of conceptual and methodological issues that commonly confound social scientists. His discussion of the meaning, effects, and remedies for missing data is thorough and clear. In particular, the section on multiple imputation is extremely well-done.

This is a reference work that will improve the scholarship of even the most rigorous researcher, and yet can serve as a wonderful introductory text on the subject of missing data for students at many levels.

38 of 42 people found the following review helpful:

4.0 out of 5 stars Dealing with an ugly problem, December 1, 2003

By 

wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews
(HALL OF FAME REVIEWER)    (TOP 500 REVIEWER)   

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This review is from: Missing Data (Quantitative Applications in the Social Sciences) (Paperback)

Beginning stats students never see the real world of dirty data. They imagine that everyone responds fully to their surveys, and that every experiment yields legible results. Oh, for such a simple world.

Allison deals with the harsh reality of incomplete data sets. The book starts with a brief description of techniques that drop incomplete data from analysis. The large majority of the book, however, discusses ways to fill in the blanks.

The author rightly points out that "imputation", or creating values to replace what's missing, is not to be taken lightly. He gives techniques, each suited to the statistical character of some set of problems, and each matched to some technique for analysis. The mathematical goal is to create proxy values that won't upset the outcome of analysis.

That is quite a bit different from finding values that represent reality. Even though imputation is supposed to be mathematically innocuous, faking experimental data seems almost immoral to me. My data sets are about as dirty as any around. Also, they have the opposite of usual form: instead of a few dozen measurements on large numbers of samples, they have thousands of measurements on relatively few individuals. I have not convinced myself that Allison's manipulations are valid in this case. I would have been grateful for more discussion of techniques for stepping around the dropouts, and for statistically deciding whether I can ignore them.

Still, this book has worthwhile content. It's brief, clear, and informative about a very important topic. I will refer back to it, but maybe not the way the author intended.