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Missing Data (Quantitative Applications in the Social Sciences) 1st Edition

by Paul D. Allison (Author)

4.8 27 ratings

Part of: Quantitative Applications in the Social Sciences (194 books)

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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.

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ISBN-10

0761916725
ISBN-13

978-0761916727
Edition

1st
Publisher

SAGE Publications, Inc
Publication date

August 13, 2001
Part of series

Quantitative Applications in the Social Sciences
Language

English
Dimensions

5.5 x 0.24 x 8.5 inches
Print length

104 pages
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Editorial Reviews

Review

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

-- Richard A. Chechile

About the Author

Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is also the founder and president of Statistical Horizons LLC which offers short courses on a wide variety of statistical topics.

After completing his doctorate 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, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.

Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data.

A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.”

Product details

Publisher ‏ : ‎ SAGE Publications, Inc; 1st edition (August 13, 2001)
Language ‏ : ‎ English
Paperback ‏ : ‎ 104 pages
ISBN-10 ‏ : ‎ 0761916725
ISBN-13 ‏ : ‎ 978-0761916727
Item Weight ‏ : ‎ 5.3 ounces
Dimensions ‏ : ‎ 5.5 x 0.24 x 8.5 inches

Best Sellers Rank: #1,304,089 in Books (See Top 100 in Books)
- #495 in General (Books)
- #670 in Statistics (Books)
- #1,272 in Social Sciences Research

Customer Reviews:
4.8 27 ratings

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Paul D. Allison

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Paul D. Allison, Ph.D., is President of Statistical Horizons LLC and Professor Emeritus of the University of Pennsylvania. 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 75 articles on topics that include linear regression, log-linear analysis, logistic regression, measurement error, inequality measures, missing data, Markov processes, and survival 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. He frequently teaches public short courses on structural equation modeling, longitudinal data analysis, and missing data. 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.

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Nathaniel and Alena

This may be all you need

Reviewed in the United States on April 26, 2013

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Paul Allison is the go-to author for sociological and social science statistical methodology for non-specialists, and this handbook is no different. He provides the information you need to determine how large a problem your particular missing data situation is and, if necessary, the best ways to go about dealing with it. Like any of the Sage Handbooks, this is not the final authority for advanced methods in missing data management, but it does cover types of missing data and various approaches, including listwise deletion, mean substitution, single and multiple imputation, and maximum likelihood methods such as FIML.

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meiweili

A little jewel.

Reviewed in the United States on March 23, 2009

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This is a small little volume that has a wonderful writing style showing the author's comfort that comes through in the LOGIC and COHERENCE that is so rare these days. Many authors write so badly about these things, as if to prove how smart and rigorous they are (resulting in a useless document). This writer simply spills the goods about how to handle missing data, and a brief explanation of why. The booklet is designed to help you solve the problem. It is working for me. The strategies are the same as I got in a sit-down meeting with one of our statistics professors (a senior guy who wrote a lot of papers on this subject). Since he had the same idea, this booklet appears to tell us how to carry it out. Great!

2 people found this helpful

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wiredweird

Dealing with an ugly problem

Reviewed in the United States on December 1, 2003

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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.

42 people found this helpful

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Express Lizard

Great book but this copy has its own missing data problem

Reviewed in the United States on September 2, 2020

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This is one of my favorite books and I was excited to receive my own copy after borrowing from a mentor. But mine arrived and something was missing...

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Allan D. Bennett

Great intuitive introduction to a highly technical subject.

Reviewed in the United States on May 18, 2017

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This monograph presents a lucid introduction to matters of handling missing data without getting bogged down in the technical details, which are addressed adequately in other works. I strongly recommend that anyone needing to learn about techniques for handling missing data read this book first to get a good intuitive grasp of the subject.

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StatMan

A very good quick read

Reviewed in the United States on September 1, 2014

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Well written book, to the point for the most part. May need to read a second time to make as some parts were a a little dense, but overall did a very good good job in explaining what to do with missing data. Never seen a textbook this small though :) it was very easy to carry around.

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William Dudley

As with all books in tehi series it is concise and easy to

Reviewed in the United States on April 27, 2016

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This is the seminal book by one of th eLeades in teh field. As with all books in tehi series it is concise and easy to understnadi

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Sophie

Good for understanding data cleaning

Reviewed in the United States on September 7, 2016

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I'm a fan of Paul and taking his courses right now. He's always clear and nice.

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