An Introduction to Categorical Data Analysis

by Alan Agresti

The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses.This Second Edition Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models A unified perspective based on generalized linear models An emphasis on logistic regression modeling An appendix that demonstrates the use of SAS for all methods An entertaining historical perspective on the development of the methods Specialized methods for ordinal data, small samples, multicategory data, and matched pairs More than 100 analyses of real data sets and nearly 300 exercisesWritten in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more.An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.

Genres: Mathematics, Textbooks

392 pages, Kindle Edition

First published January 1, 1996

Reviews

[Nur · Rating 4 out of 5]

To any Epidemiology PhD student. Very easy wording, applied non-technical style.

[Brendan · Rating 3 out of 5]

Perfectly fine. Like many of these textbooks, it is plagued by toy problems which don’t give you an understanding of how to use the ideas. Projects with messy datasets in the back and open ended responses would be ideal, but I suppose that is why we have Kaggle.

[Abrar · Rating 4 out of 5]

This book is suitable for those who are interesting to analyse categorical data.
it's very useful and easy to understand.