Ordinal Data Modeling
(Sprache: Englisch)
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...
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Klappentext zu „Ordinal Data Modeling “
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.
Inhaltsverzeichnis zu „Ordinal Data Modeling “
Review of Classical and Bayesian Inference.- Review of Bayesian Computation.- Regression Models for Binary Data.- Regression Models for Ordinal Data.- Analyzing Data from Multiple Raters.- Item Response Modeling.- Graded Response Models: A Case Study of Undergraduate Grade Data.
Bibliographische Angaben
- Autoren: Valen E. Johnson , James H. Albert
- 2013, Softcover reprint of the original 1st ed. 1999, X, 258 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1475772904
- ISBN-13: 9781475772906
Sprache:
Englisch
Pressezitat
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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