In recent years methods for analyzing categorical data have matured considerably in their development. There has been a tremendous increase in the publication of research articles on this topic. Several books on categorical data analysis have introduced the methods to audiences of nonstatisticians as well as to statisticians, and the methods are now used frequently by researchers in areas as diverse as sociology, public health, and wildlife ecology. Yet some types of methods are still in the process of development, such as methods for clustered data, Bayesian methods, and methods for sparse data sets with large numbers of variables.
What distinguishes this book from others on categorical data analysis is its emphasis on methods for response variables having ordered categories, that is, ordinal variables. Specialized models and descriptive measures are discussed that use the information on ordering efficiently. These ordinal methods make possible simpler description of the data and permit more powerful inferences about population characteristics than do models for nominal variables that ignore the ordering information.
This is the second edition of a book published originally in 1984. At that time many statisticians were unfamiliar with the relatively new modeling methods for categorical data analysis, so the early chapters of the first edition introduced generalized linear modeling topics such as logistic regression and loglinear models. Since many books now provide this information, this second edition takes a different approach, assuming that the reader already has some familiarity with the basic methods of categorical data analysis. These methods include descriptive summaries using odds ratios, inferential methods including chi-squared tests of the hypotheses of independence and conditional independence, and logistic regression modeling, such as presented in Chapters 1 to 6 of my books An Introduction to Categorical Data Analysis (2nd ed., Wiley, 2007) and Categorical Data Analysis (2nd ed., Wiley, 2002).
On an ordinal scale, the technical level of this book is intended to fall between that of the two books just mentioned. I intend the book to be accessible to a broad audience, particularly professional statisticians and methodologists in areas such as public health, the pharmaceutical industry, the social and behavioral sciences, and business and government. Although there is some discussion of the underlying theory, the main emphasis is on presenting various ordinal methodologies. Thus, the book has more discussion of interpretation and application of the methods than of the technical details. However, I also intend the book to be useful to specialists who may want to become aware of recent research advances, to supplement the background provided. For this purpose, the Notes section at the end of each chapter provides supplementary technical comments and embellishments, with emphasis on references to related research literature.
The text contains significant changes from and additions to the first edition, so it seemed as if I were writing a new book! As mentioned, the basic introductions to logistic regression and loglinear models have been removed. New material includes chapters on marginal models and random effects models for clustered data (Chapters 9 and 10) and Bayesian methods (Chapter 11), coverage of additional models such as the stereotype model, global odds ratio models, and generalizations of cumulative logit models, coverage of order-restricted inference, and more detail throughout on established methods.