A valuable new edition of a standard reference
The use of statistical methods for categorical data has increased
dramatically, particularly for applications in the biomedical and social
sciences. An Introduction to Categorical Data Analysis, Third Edition
summarizes these methods and shows readers how to use them using
software. Readers will find a unified generalized linear models approach
that connects logistic regression and loglinear models for discrete data
with normal regression for continuous data.
Adding to the value in the new edition is:
- Illustrations of the use of R software to perform all the analyses in
the book
- A new chapter on alternative methods for categorical data, including
smoothing and regularization methods (such as the lasso), classification
methods such as linear discriminant analysis and classification trees,
and cluster analysis
- New sections in many chapters introducing the Bayesian approach for
the methods of that chapter
- More than 70 analyses of data sets to illustrate application of the
methods, and about 200 exercises, many containing other data sets
- An appendix showing how to use SAS, Stata, and SPSS, and an appendix
with short solutions to most odd-numbered exercises
Written in an applied, nontechnical style, this book illustrates the
methods using a wide variety of real data, including medical clinical
trials, environmental questions, drug use by teenagers, horseshoe crab
mating, basketball shooting, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Third Edition is an
invaluable tool for statisticians and biostatisticians as well as
methodologists in the social and behavioral sciences, medicine and
public health, marketing, education, and the biological and agricultural
sciences.
