Ordinal Data Modeling / Edition 1

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Overview

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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Editorial Reviews

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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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Product Details

  • ISBN-13: 9780387987187
  • Publisher: Springer New York
  • Publication date: 3/30/1999
  • Series: Statistics for Social and Behavioral Sciences Series
  • Edition description: 1st ed. 1999. Corr. 2nd printing 2000
  • Edition number: 1
  • Pages: 258
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.75 (d)

Table of Contents

Preface
1 Review of Classical and Bayesian Inference 1
2 Review of Bayesian Computation 33
3 Regression Models for Binary Data 75
4 Regression Models for Ordinal Data 126
5 Analyzing Data from Multiple Raters 158
6 Item Response Modeling 182
7 Graded Response Models: A Case Study of Undergraduate Grade Data 215
App Software for Ordinal Data Modeling 239
References 249
Index 255
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