Statistical Group Comparison / Wiley Series in Probability and Statistics (PDF)
(Sprache: Englisch)
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...
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...
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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.
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.
Inhaltsverzeichnis zu „Statistical Group Comparison / Wiley Series in Probability and Statistics (PDF)“
Preface. 1. Introduction. 1.1 Rationale for Statistical Comparison. 1.2 Comparative Research in the Social Sciences. 1.3 Focus of the Book. 1.4 Outline of the Book. 2. Statistical Foundation for Comparison. 2.1 A System for Statistical Comparison. 2.2 Test Statistics. 2.3 What to Compare? 3. Comparison in Linear Models. 3.1 Introduction. 3.2 An Example. 3.3 Some Preliminary Considerations. 3.4 The Linear Model. 3.5 Comparing Two Means. 3.6 ANOVA. 3.7 Multiple Comparison Methods. 3.8 ANCOVA. 3.9 Multiple Linear Regression. 3.10 Regression Decomposition. 3.11 Which Linear Method to Use? 4. Nonparametric Comparison. 4.1 Nonparametric Tests. 4.2 Resampling Methods. 4.3 Relative Distribution Methods. 5. Comparison of Rates. 5.1 The Data. 5.2 Standardization. 5.3 Decomposition. 6. Comparison in Generalized Linear Models. 6.1 Introduction. 6.2 Comparing Generalized Linear Models. 6.3 A Logit Model Example. 6.4 A Hazard Rate Model Example. 6.A Data Used in Section 6.4. 7. Additional Topics of Comparison in Generalized Linear Models. 7.1 Introduction. 7.2 GLM for Matched Case-Control Studies. 7.3 Dispersion Heterogeneity. 7.4 Bayesian Generalized Linear Models. 7.A The Data for the n : m Design. 8. Comparison in Structural Equation Modeling. 8.1 Introduction. 8.2 Statistical Background. 8.3 Mean and Covariance Structures. 8.4 Group Comparison in SEM. 8.5 An Example. 8.A Examples of Computer Program Listings. 9. Comparison with Categorical Latent Variables. 9.1 Introduction. 9.2 Latent Class Models. 9.3 Latent Trait Models. 9.4 Latent Variable Models for Continuous Indicators. 9.5 Casual Models with Categorical Latent variables. 9.6 Comparison with Categorical Latent Variables. 9.7 Examples. 9.A Software for Categorical Latent Variables. 9.B Computer Program Listings for the Examples. 10. Comparison in Multilevel Analysis. 10.1 Introduction. 10.2 An Introduction to Multilevel Analysis. 10.3 The Basics of the Linear Multilevel Model. 10.4 The Basics of the Generalized Linear
... mehr
Multilevel Model. 10.5 Group as an External Variable in Multilevel Analysis. 10.6 The Relation between Multilevel Analysis and Group Comparison. 10.7 Multiple Membership Models. 10.8 Summary. 10.A Software for Multilevel Analysis. 10.B SAS Program Listings for GLMM Examples. References. Index.
... weniger
Autoren-Porträt von Tim Futing Liao
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.
Bibliographische Angaben
- Autor: Tim Futing Liao
- 2011, 1. Auflage, 240 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118150619
- ISBN-13: 9781118150610
- Erscheinungsdatum: 20.09.2011
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