Items related to Statistical Group Comparison
Synopsis
An incomparably useful examination of statistical methods for comparison
The nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not independent of the outcome.
Statistical Group Comparison brings together a broad range of statistical methods for comparison developed over recent years. The book covers a wide spectrum of topics from the simplest comparison of two means or rates to more recently developed statistics including double generalized linear models and Bayesian as well as hierarchical methods. Coverage includes:
* Testing parameter equality in linear regression and other generalized linear models (GLMs), in order of increasing complexity
* Likelihood ratio, Wald, and Lagrange multiplier statistics examined where applicable
* Group comparisons involving latent variables in structural equation modeling
* Models of comparison for categorical latent variables
Examples are drawn from the social, political, economic, and biomedical sciences; many can be implemented using widely available software. Because of the range and the generality of the statistical methods covered, researchers across many disciplines-beyond the social, political, economic, and biomedical sciences-will find the book a convenient reference for many a research situation where comparisons may come naturally.
"synopsis" may belong to another edition of this title.
About the Author
TIM FUTING LIAO, PhD, is Associate Professor of Sociology and Statistics at the University of Illinois at Urbana-Champaign. He currently teaches as Senior Lecturer in Sociology at the University of Essex, UK.
From the Back Cover
An incomparably useful examination of statistical methods for comparison
The nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not independent of the outcome.
Statistical Group Comparison brings together a broad range of statistical methods for comparison developed over recent years. The book covers a wide spectrum of topics from the simplest comparison of two means or rates to more recently developed statistics including double generalized linear models and Bayesian as well as hierarchical methods. Coverage includes:
* Testing parameter equality in linear regression and other generalized linear models (GLMs), in order of increasing complexity
* Likelihood ratio, Wald, and Lagrange multiplier statistics examined where applicable
* Group comparisons involving latent variables in structural equation modeling
* Models of comparison for categorical latent variables
Examples are drawn from the social, political, economic, and biomedical sciences; many can be implemented using widely available software. Because of the range and the generality of the statistical methods covered, researchers across many disciplines-beyond the social, political, economic, and biomedical sciences-will find the book a convenient reference for many a research situation where comparisons may come naturally.
From the Inside Flap
An incomparably useful examination of statistical methods for comparison
The nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not independent of the outcome.
Statistical Group Comparison brings together a broad range of statistical methods for comparison developed over recent years. The book covers a wide spectrum of topics from the simplest comparison of two means or rates to more recently developed statistics including double generalized linear models and Bayesian as well as hierarchical methods. Coverage includes:
* Testing parameter equality in linear regression and other generalized linear models (GLMs), in order of increasing complexity
* Likelihood ratio, Wald, and Lagrange multiplier statistics examined where applicable
* Group comparisons involving latent variables in structural equation modeling
* Models of comparison for categorical latent variables
Examples are drawn from the social, political, economic, and biomedical sciences; many can be implemented using widely available software. Because of the range and the generality of the statistical methods covered, researchers across many disciplines-beyond the social, political, economic, and biomedical sciences-will find the book a convenient reference for many a research situation where comparisons may come naturally.
"About this title" may belong to another edition of this title.
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Statistical Group Comparison (Hardcover)
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John Wiley & Sons Inc, New York, 2002
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ISBN 13: 9780471386469
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Hardcover. Condition: new. Hardcover. An incomparably useful examination of statistical methods for comparison The nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not independent of the outcome. Statistical Group Comparison brings together a broad range of statistical methods for comparison developed over recent years. The book covers a wide spectrum of topics from the simplest comparison of two means or rates to more recently developed statistics including double generalized linear models and Bayesian as well as hierarchical methods. Coverage includes: * Testing parameter equality in linear regression and other generalized linear models (GLMs), in order of increasing complexity * Likelihood ratio, Wald, and Lagrange multiplier statistics examined where applicable * Group comparisons involving latent variables in structural equation modeling * Models of comparison for categorical latent variables Examples are drawn from the social, political, economic, and biomedical sciences; many can be implemented using widely available software. Because of the range and the generality of the statistical methods covered, researchers across many disciplines-beyond the social, political, economic, and biomedical sciences-will find the book a convenient reference for many a research situation where comparisons may come naturally. The nature of doing science, be it natural or social, inevitably calls for comparison, and statistical methods are at the heart of such comparison. This book covers many topics from the simplest comparison of two means to the more developed statistics, including double generalized linear models and Bayesian, as well as hierarchical methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780471386469
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