Ordinal Data Modeling [electronic resource] /
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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Format: | Texto biblioteca |
Language: | eng |
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New York, NY : Springer New York,
1999
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Subjects: | Statistics., Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law., |
Online Access: | http://dx.doi.org/10.1007/b98832 |
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KOHA-OAI-TEST:2230752018-07-31T00:01:54ZOrdinal Data Modeling [electronic resource] / Johnson, Valen E. author. Albert, James H. author. SpringerLink (Online service) textNew York, NY : Springer New York,1999.engOrdinal 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.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.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.Statistics.Statistics.Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.Springer eBookshttp://dx.doi.org/10.1007/b98832URN:ISBN:9780387227023 |
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Statistics. Statistics. Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. Statistics. Statistics. Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. |
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Statistics. Statistics. Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. Statistics. Statistics. Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. Johnson, Valen E. author. Albert, James H. author. SpringerLink (Online service) Ordinal Data Modeling [electronic resource] / |
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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. |
format |
Texto |
topic_facet |
Statistics. Statistics. Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. |
author |
Johnson, Valen E. author. Albert, James H. author. SpringerLink (Online service) |
author_facet |
Johnson, Valen E. author. Albert, James H. author. SpringerLink (Online service) |
author_sort |
Johnson, Valen E. author. |
title |
Ordinal Data Modeling [electronic resource] / |
title_short |
Ordinal Data Modeling [electronic resource] / |
title_full |
Ordinal Data Modeling [electronic resource] / |
title_fullStr |
Ordinal Data Modeling [electronic resource] / |
title_full_unstemmed |
Ordinal Data Modeling [electronic resource] / |
title_sort |
ordinal data modeling [electronic resource] / |
publisher |
New York, NY : Springer New York, |
publishDate |
1999 |
url |
http://dx.doi.org/10.1007/b98832 |
work_keys_str_mv |
AT johnsonvaleneauthor ordinaldatamodelingelectronicresource AT albertjameshauthor ordinaldatamodelingelectronicresource AT springerlinkonlineservice ordinaldatamodelingelectronicresource |
_version_ |
1756270524708683776 |