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Categorical Data Analysis (Wiley Series in Probability and Statistics) Hardcover – July 22, 2002

ISBN-13: 978-0471360933 ISBN-10: 0471360937 Edition: 2nd

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Product Details

  • Series: Wiley Series in Probability and Statistics (Book 359)
  • Hardcover: 734 pages
  • Publisher: Wiley-Interscience; 2 edition (July 22, 2002)
  • Language: English
  • ISBN-10: 0471360937
  • ISBN-13: 978-0471360933
  • Product Dimensions: 6.5 x 1.5 x 9.4 inches
  • Shipping Weight: 2.4 pounds
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #596,130 in Books (See Top 100 in Books)

Editorial Reviews

Review

“…the essential reference text for statisticians…comprehensive and readable…” (Statistical Methods in Medical Research, Vol. 14, 2005)

"...careful, thorough, up-to-date volume...the new content and emphases in the second edition are sufficient to justify its purchase even by someone who already owns the first edition." (Journal of the American Statistical Association, June 2004)

"I liked this revised edition and recommend it highly to statisticians and graduate students." (Journal of Statistical Computation & Simulation, March 2004)

"...a highly satisfactory text on methods for categorical response variables...more complete and technical [than the First Edition]..." (IIE Transactions on Quality and Reliability Engineering)

"If you own the 1E, you absolutely need to upgrade to the 2E. If you do any analysis of categorical data, this is an essential desktop reference..." (Technometrics, Vol. 45, No. 1, February 2003)

"...this classic book is substantially modified and expanded..." (International Journal of General Systems, Vol. 32, 2003)

"...it is a total delight reading this book, which should be considered as the current standard textbook for teaching analysis of categorical data." (Pharmaceutical Research, Vol. 20, No. 6, June 2003)

"...written in a highly scientific but vivid style, intelligible for all researchers in that field...simply expressed, grand..." (Zentralblatt Math, 2004)

From the Publisher

Summarizes methods used for the analysis of categorical data, including many recently developed techniques. The emphasis is on loglinear and logit modeling techniques, which share many features with linear model methods for continuous variables. Incorporated into the exposition is interesting historical information (and controversies) on the development of categorical data analysis. Chapters 1-7 cover bivariate categorical data and loglinear and logit model building; chapters 8-11 discuss applications and methods; chapters 12 and 13 address theoretical foundations. --This text refers to an out of print or unavailable edition of this title.

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Most Helpful Customer Reviews

31 of 31 people found the following review helpful By Peter Flom on July 22, 2003
Format: Hardcover
This is a very demanding, thorough, and clear description of just about everything anyone could want to know on the subject. The second edition is considerably more rigorous than the first. Agresti stresses that logistic models are one kind of generalized linear model. This offers solid connections to many other models, but places corresponding demands on the reader. In particular, Chapter 4 is difficult going, but might be skipped or skimmed on first reading.
Given the mathematical level and rigor, this is a remarkably clear book. Anyone who analyzes categorical data on a regular basis should read it and have it on his or her shelf.
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27 of 27 people found the following review helpful By Michael R. Chernick on January 23, 2008
Format: Hardcover
When this book came out in 1990 it was the first book to provide a truely modern treatment of categorical data analysis for both ordinal and nominal data. It provides an excellent treatment of the asymptotic theory for binary and multinomial data. It is extremely well written and is still a favorite of statisticians and practitioners. Because of its popularity and continued value, it should soon be added to the Wiley Classic series.
This is the first book to take the regression approach to categorical data analysis tieing the subject to the methods and theory of the generalized linear models. It also was one of the first to show the modern practicality of exact permutation methods.

The only drawback of this book is that it is 11 years old and there have been many interesting and relevant research developments in computer-intensive methods, analysis of missing data and mixed effects linear models to make a revision useful. Some of the latest developments can be found in Lloyd's new book "Statistical Analysis of Categorical Data" that was recently published by Wiley.

Agresti provides clear advice and also gives a nice historical perspective on the development of the subject. The book is authoritative and includes numerous relevant references. Each chapter contains many exercises and a wealth of practical examples for illustration of the techniques. This is a good text from both practical and theoretical perspectives. It is excellent for a graduate level course on categorical data analysis.
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20 of 28 people found the following review helpful By H. Ward on May 3, 2006
Format: Hardcover
In the theoretical sense, this book provides a very thorough overview of categorical data analysis. However, this book should not be used as a reference for the scientist needing to do the occasional number crunching of categorical data. The examples are vague and the tests are not well explained. If you want to derive the tests, this book is for you. If you're not a statistician at heart and just want the answer, I suggest looking at Conover's "Practical Nonparametric Statistics" for a good explanation of which tests to use and how to use them.
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2 of 2 people found the following review helpful By Sunny on August 20, 2008
Format: Hardcover
The text is comprehensive in covering categorical data. Other reviews make this clear so I wanted to focus on the following. I was able to understand more general topics in statistics because of Agresti's depth of coverage on CDA. For example, for repeated measurements, Agresti clearly explains marginal models, conditional models, and generalized estimating equations. When I needed to understand these topics, I used this text because I have not found clear explanations elsewhere. In addition, SAS code and R code is available for the examples presented.
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1 of 1 people found the following review helpful By tmb2118 on February 20, 2009
Format: Hardcover
This book is the standard for this material. It is clear, concise and comes with enough examples to assure the reader of the empirical importance of all the theory.
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5 of 7 people found the following review helpful By kmir on October 17, 2007
Format: Hardcover Verified Purchase
Please read this in addition to the other reviews! I agree with the other reviewers except on one aspect: I found the style of writing a little bit choppy at times. The author uses short sentences when a few connecting words like e.g. "because", "due to", would have made understanding a little easier. Also, examples are not integrated optimally into the text so that there seems to be a gap between abstract conceptual explanations and the examples.
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