Customer Reviews

An Introduction to Categorical Data Analysis

5 star:		(8)
4 star:		(2)
3 star:		(1)
2 star:		(1)
1 star:		(1)

 

 

Agresti is recognized as one of the leading experts in categorical data analysis and his advanced book has received treemendous acclaim. This book has much of the important content of the advanced book but watered down a little to be understandable to a broader audience. Alan Agresti is very good at doing that and therefore this book deserves high praise.

The larger, older, more complex Agresti (to which many of the reviewers have referred) is written in formulas. His newer book, the subject of review, is written in words. For a large group of readers, "Introduction" is perfectly suitable. However, those who are really interested in the mathematical concepts, should buy the older Agresti title. My personal opinion is that they are good companions and that the introductory book is the place to start, unless you know that you will be unhappy with the light treatment of the mathematical issues.

Agresti took most of the material from his big book, _Categorical Data Analysis_, and took out all the justifications. What's left is _An Introduction to Categorical Data Analysis_, and no math. If you enjoy having results fed to you so that you can memorise then, read this book. Otherwise, I concur with the other reader: buy this book's big brother.

Clear, concise,and extremely well written and edited ( in the 99% tail for Statistics books!). Great problems. Do them and you will learn quickly and easily. Contains a gem: A chapter on the modern history of Statistics including the egos, feuds, and angry debates and Pearson's great error, which he arrogantly refused to admit. But, a big disappointment: the book does NOT give enough formulas or guidance to the use the usual software. The author condescendingly underates the average user's math ability and understanding. Conclusion: The book is interesting, and can be read as a novel, but it is fluff --- you really can't use the book to use your software. Essentially an entertaining waste of time. Buy Agresti's industrial strength book, Categorial Data Analysis instead.

This book gives the reader a basic foundation for understanding statistical analysis using generalized linear models (GLMs), focusing on the specialized cases of logistic regression and log-linear models. Concepts are clearly presented and accompanied by figures and appropriate examples. Hypothesis testing and test statistics are carefully outlined by the author. This is the most readable and useful statistics text I have encountered, and should be an invaluable resource to anyone dealing with categorical response data.

Agresti's book 'Introduction to Categorical Data Analysis' has been very valuable for my research and understanding of logistics regression techniques. I am not a statistics major and so I greatly appreciate his use of examples to discuss the concepts. Rather than getting lost in equations with tons of vectors and matrices, Agresti's book focuses on the core concepts and methodologies you need to prepare your data, set up the models and interpret the results. The companion webpage has the SAS code for most of the examples so you can replicate what he did. Highly Recommended!

This is an excellent resource. It's clear, concise, and informative. It provides readers w/ the perfect mix of conceptual and procedural knowledge. As other reviewers have suggested, it may be a little too light for hard-core statisticians, but it's perfect for scientists who deal w/ categorical dependent variables. I use it all the time!

In response to some of the other reviewers saying this book doesn't emphasize the mathematics enough: if you are a statistics graduate student and this is your assigned text for this topic, you have every right in the world to be miffed. This book wasn't written for you. It really is a great book though for the undergraduate or graduate non-stats student who is just looking to pick up some techniques for their research. It works well as a supplement to lectures and is well written enough for an autodidact to find useful.

As a mathematician, I'm used to learning material through definitions, theorems and then problems. This book was a radical departure in that most of the concepts are taught through real life data sets. For example, logistic regression was taught using a set of data on crabs.

There is unfortunately no mathematical theory in this book. All results are attained through SAS using computational ML methods. In this sense, it's basically a how-to guide to categorical data analysis for researches in general. And it's a great book for what it is.

I am a statistics graduate student and have read this book, the first edition of this book, and parts of the full version (Categorical Data Analysis - Agresti 2002). This edition (2nd) of Introduction to Categorical Data Analysis is not as good as the first edition or the 2002 edition. The examples are hard to follow and the SAS output is difficult to replicate in some cases. Do yourself a favor and use the 2002 full version. It will save you a lot of time and frustration.Categorical Data Analysis (Wiley Series in Probability and Statistics)