Categorical Data Analysis describes the most important methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well.Special features of the book include:Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applicationsPrescriptions for how ordinal variables should be treated differently than nominal variablesDerivations of basic asymptotic and fixed-sample-size inferential methodsDiscussion of exact small sample proceduresMore than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball gamesMore than 400 exercises to facilitate interpretation and application of methodsCategorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes and the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians and professional researchers. Read Less