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Title: Missing Data : Series: Quantitative Applications in the Social Sciences by Paul D. Allison ISBN: 0-7619-1672-5 Publisher: Sage Publications Pub. Date: 13 August, 2001 Format: Paperback Volumes: 1 List Price(USD): $15.95 |
Average Customer Rating: 4.5 (2 reviews)
Rating: 4
Summary: Dealing with an ugly problem
Comment: 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.
Rating: 5
Summary: Fabulous primer on handling missing data
Comment: 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.
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Title: Interaction Effects in Logistic Regression by James Jaccard ISBN: 0761922075 Publisher: Sage Publications Pub. Date: 15 February, 2001 List Price(USD): $15.95 |
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Title: Statistical Analysis With Missing Data by Roderick J. A. Little, Donald B. Rubin ISBN: 0471183865 Publisher: Wiley-Interscience Pub. Date: 23 August, 2002 List Price(USD): $99.95 |
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Title: Applied Logistic Regression Analysis by Scott Menard ISBN: 0761922083 Publisher: Sage Publications Pub. Date: October, 2001 List Price(USD): $15.95 |
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Title: Interaction Effects in Multiple Regression by James Jaccard, Robert Turrisi ISBN: 0761927425 Publisher: Sage Publications Pub. Date: 05 March, 2003 List Price(USD): $15.95 |
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Title: Generalized Linear Models : A Unified Approach by Jeff Gill ISBN: 0761920552 Publisher: Sage Publications Pub. Date: 07 August, 2000 List Price(USD): $15.95 |
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