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Ordinal Data Modeling (Statistics for Social and Behavioral Sciences) Hardcover – December 15, 2000

ISBN-13: 000-0387987185 ISBN-10: 0387987185 Edition: Corrected

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Product Details

  • Series: Statistics for Social and Behavioral Sciences
  • Hardcover: 258 pages
  • Publisher: Springer; Corrected edition (December 15, 2000)
  • Language: English
  • ISBN-10: 0387987185
  • ISBN-13: 978-0387987187
  • Product Dimensions: 6.1 x 0.8 x 9.2 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #3,374,949 in Books (See Top 100 in Books)

Editorial Reviews


From the reviews:


"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)."

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Most Helpful Customer Reviews

26 of 26 people found the following review helpful By Michael R. Chernick on January 23, 2008
Format: Hardcover
This book provides both the Bayesian and classical approaches to ordinal data analysis but is unique in emphasizing the Bayesian approach and the latest advances. The authors are academic statisticians and the text is designed for a graduate level course for statistics or social science majors. It includes some very well written introductory material on these two forms fo statistical inference.
The mathematical level is intermediate but is written in a clear way to be accessible to social science students. This is also a good reference book for statisticians especially those involved in educational testing.

Markov chain Monte Carlo methods are provided along with some programmed algorithms for doing Gibbs sampling. A website is available to help the reader get access to datasets and software to implement the procedures.

Although the offer of software is nice, the authors neglect to mention the BUGS software that has been developed in the UK to handle MCMC problems. BUGS or the new window based WinBUGS is easily accessible to the reader and provides a lot of additional modeling aids including diagnostics.

The book covers a lot of interesting and applications oriented topics including logistic regression, ordinal regression, item response models, graded response models and the analysis of ROC curves. Concepts are illustrated and techniques demonstrated through real problems.
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