In recent years, the use of specialized statistical methods for categorical data has
increased dramatically, particularly for applications in the biomedical and social
sciences. Partly this reflects the development during the past few decades of
sophisticated methods for analyzing categorical data. It also reflects the increasing
methodological sophistication of scientists and applied statisticians, most of
whom now realize that it is unnecessary and often inappropriate to use methods
for continuous data with categorical responses.
This book presents the most important methods for analyzing categorical data. It
summarizes methods that have long played a prominent role, such as chi-squared
tests. It gives special emphasis, however, to modeling techniques, in particular to
logistic regression.
The presentation in this book has a low technical level and does not require familiarity
with advanced mathematics such as calculus or matrix algebra. Readers should
possess a background that includes material from a two-semester statistical methods
sequence for undergraduate or graduate nonstatistics majors. This background should
include estimation and significance testing and exposure to regression modeling.
This book is designed for students taking an introductory course in categorical data
analysis, but I also have written it for applied statisticians and practicing scientists
involved in data analyses. I hope that the book will be helpful to analysts dealing with
categorical response data in the social, behavioral, and biomedical sciences, as well
as in public health, marketing, education, biological and agricultural sciences, and
industrial quality control.