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Presents a variety of new sophisticated models for the analysis of two- and multidimensional-contingency tables
Real-world applications to life sciences, education, social and political sciences, notably market research, and opinion surveys
Emphasis is on applications and methods of fitting models using standard statistical packages, such as SPSS, S-PLUS, R, and BUGS, as well as on interpretation of the results
Up-to-date supplementary material is available on the author's website
For a broad audience of advanced undergraduates, graduate students, and practitioners in statistics, biosciences, social sciences, education, and economics
May be used as a textbook for a course on categorical data analysis
Combining theory and applications, this book presents models and methods for the analysis of two‐ and multi‐dimensional contingency tables. The author uses a threefold approach: fundamental models and related inferences are presented, their interpretational aspects are highlighted, and their practical usefulness is demonstrated. Throughout, practical guidance for using R is provided along with a comprehensive R-functions web-appendix.
Contingency tables arise in diverse fields, including the life, pedagogic, social and political sciences. They also play a prominent role in market research and opinion surveys. The analysis of contingency tables can provide insight into essential structures, relevant quantities and their interactions, and thus leads to improved decision-making.
Special features include:
· A motivating example for each topic
· Applications and implementations in R for all models discussed
· Emphasis on association and symmetry models
· Extensive bibliography
· Up-to-date supplementary material available on the author’s website
An excellent reference for graduate students, researchers, and practitioners in statistics as well as in the biosciences and social sciences, Contingency Table Analysis may also be used as a supplementary textbook for courses on categorical data analysis with emphasis on special models for ordinal data. Prerequisites include basic background knowledge of statistical inference.