Statistical science’s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.
About the Author
ALAN AGRESTI, PhD, is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida and Visiting Professor in the Department of Statistics at Harvard University. A Fellow of the American Statistical Association and the Institute of Mathematical Statistics, Dr. Agresti has published extensively on the topic of categorical data analysis and has presented lectures and short courses on the subject in more than thirty countries. He is the author of Categorical Data Analysis, Second Edition and An Introduction to Categorical Data Analysis, Second Edition, both published by Wiley.
New to Edition
Now with a revised structure, the more advanced material can be found in the ends of chapters
Coverage has been updated to feature recent methods such as log linear, logit, and multinomial logistic regression; repeated measurement ordinal data; and clustering.
Section notes have been added to provide reference to further details and to recent relevant research.
All of the exercises have been changed, updated, and streamlined. They now require data analyses and deal with interpretations of the methods.
A related Web site features data sets, SAS programs, and additional supplemental material.
Coverage has been updated to feaature recent methods such as log linear, logit, and multinomial logistic regression; repeated measurement ordinal data; and clustering.
Section notes have been added to provide reference to further details and to recent relevant research.
The use of existing statistical computer packages for implementing the methods is explained; these include SAS, SPSS, GLIM and R subroutines.
All of the exercises have been changed, updated, and streamlined. They now require data analyses and deal with interpretations of the methods.
An extensive bibliography includes articles dealing with the analysis of ordinal categorical data, pulling together research in the last two decades from widely divergent sources.
A related Web site features data sets, SAS programs, and additional supplemental material.
