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About this book
Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. Written for graduate students and researchers in the statistical and social sciences, this book describes a coherent framework for understanding binary and ordinal regression models, item response models, graded response models, and ROC analyses, and for exposing the close connection between these models. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.
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Keywords
Table of contents (7 chapters)
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Front Matter
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Back Matter
Reviews
From the reviews:
STATISTICAL METHODS IN MEDICAL RESEARCH
"The book is an excellent introduction to Bayesian methods of original data modeling…the book is well written and all concepts are presented in a clear manner. Notation is consistent throughout the book and ideas in later chapters logically build on previous material. Fundamental concepts discussed by the authors are well presented. The authors are particularly savvy at connecting new ideas to previous knowledge. This book would make an excellent supplementary text to an individual doing research in these areas…This book does provide valuable information about modeling original data using Bayesian methods. This text provides a valuable addition to a researcher’s library (especially those working in psychometrics or educational statistics)."
Authors and Affiliations
Bibliographic Information
Book Title: Ordinal Data Modeling
Authors: Valen E. Johnson, James H. Albert
Series Title: Statistics for Social and Behavioral Sciences
DOI: https://doi.org/10.1007/b98832
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1999
Hardcover ISBN: 978-0-387-98718-7Published: 30 March 1999
Softcover ISBN: 978-1-4757-7290-6Published: 14 April 2013
eBook ISBN: 978-0-387-22702-3Published: 06 April 2006
Series ISSN: 2199-7357
Series E-ISSN: 2199-7365
Edition Number: 1
Number of Pages: X, 258