- 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 9.5 inches
- Shipping Weight: 2.4 pounds (View shipping rates and policies)
- Average Customer Review: 3.9 out of 5 stars See all reviews (17 customer reviews)
- Amazon Best Sellers Rank: #1,149,365 in Books (See Top 100 in Books)
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Categorical Data Analysis (Wiley Series in Probability and Statistics) 2nd Edition
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“…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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Top Customer Reviews
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.
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.
Don't waste your money. If I could give this book 0 stars I would. It seems a money-making scheme if you ask me.
The 2 important things reading this book has taught me to look for are (1) the role of cofounders in categorical analysis (see the example in section 2.3.2 for an illustration) and (2) effective groupings, when one of the entries in the contingency table is scarce