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An Introduction to Categorical Data Analysis, 2nd Edition

ISBN: 978-0-471-22618-5

March 2007

400 pages

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Description
An Introduction to Categorical Data Analysis, Second Edition presents an introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role such as chi-squared tests and measures of association. It provides special emphasis, however, to logistic regression and loglinear modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition presents new methods for clustered data, which are increasingly common in longitudinal studies, for example. Two new chapters discuss these methods along with improvements in major software. Chapter 10 deals with marginal models, including the generalized estimating equations (GEE) approach. Chapter 11 deals with random effects models through generalized linear models. Earlier chapters and appendices are updated.
About the Author
ALAN AGRESTI, PhD, is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on categorical data methods in thirty countries. Dr. Agresti was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association in 2003. He is the author of two advanced texts, including the bestselling Categorical Data Analysis (Wiley) and is also the coauthor of Statistics: The Art and Science of Learning from Data and Statistical Methods for the Social Sciences.
New to Edition
  • Second edition of one of the best-selling books on categorical data analysis, from one of the most authoritative authors in the field.
  • Features new chapters on marginal models, including the generalized estimating equations (GEE) approach and random effects models.
  • Already existing material, including SAS and SPSS data sets, is updated to reflect technical advances since the publication of the first edition.
  • Introductory material on generalized linear models will now include information on negative binomial regression.
  • Written on a relatively low technical level and does not require familiarity with advanced mathematics such as calculus or matrix algebra.