Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. 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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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)."
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analysis of ordinal data from both Bayesian and frequentist perspectives,
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This review is from: Ordinal Data Modeling (Statistics for Social and Behavioral Sciences) (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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In the fall of 1997, the faculty and administration of Duke University decided to review and revise the university's curriculum. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
data matrix stat, prerequisite probability course, achievement index rank, achievement indices, item response curve, grade cutoffs, ordinal probit model, binary regression models, ordinal data models, category cutoffs, graded response models, exact posterior density, item discrimination parameters, adjusted deviance residuals, normal proposal density, ordinal regression models, latent residuals, binary residuals, rater variances, mathematics placement test, item response models, deviance contributions, normal scores plot, proportional odds model, latent data Key Phrases - Capitalized Phrases (CAPs): (learn more)
Duke University, Continuation of Exercise, Los Angeles, Monte Carlo, Parameter Post New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
In the fall of 1997, the faculty and administration of Duke University decided to review and revise the university's curriculum. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
data matrix stat, prerequisite probability course, achievement index rank, achievement indices, item response curve, grade cutoffs, ordinal probit model, binary regression models, ordinal data models, category cutoffs, graded response models, exact posterior density, item discrimination parameters, adjusted deviance residuals, normal proposal density, ordinal regression models, latent residuals, binary residuals, rater variances, mathematics placement test, item response models, deviance contributions, normal scores plot, proportional odds model, latent data Key Phrases - Capitalized Phrases (CAPs): (learn more)
Duke University, Continuation of Exercise, Los Angeles, Monte Carlo, Parameter Post New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 23
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17
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- Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) by Andrew Gelman on page 27, and Back Matter
- Bayesian Statistics: An Introduction (Arnold Publication) by Peter M. Lee on page 27, and Back Matter
- Statistics: A Bayesian Perspective by Donald A. Berry on page 27, and Back Matter
- Bayesian Inference in Statistical Analysis (Wiley Classics Library) by George E. P. Box on page 27, and Back Matter
- The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics) by Christian Robert on page 27, and Back Matter
See all 23 books this book cites
- Applied Bayesian Modelling (Wiley Series in Probability and Statistics) by Professor Peter Congdon on page 169, page 270, and page 359
- Health Research by Catherine Berglund in Back Matter (1), and Back Matter (2)
- Generalizability Theory by Robert L. Brennan in Front Matter
- Public Policy and Statistics: Case Studies from RAND (Statistics for Social and Behavioral Sciences) by Sally C. Morton in Front Matter
- Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics) by Professor Peter Congdon on page 376
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