Hierarchical Linear Models: Applications and Data Analysis Methods: Advanced Quantitative Techniques in the Social Sciences, cartea 1
Autor Stephen W. Raudenbush, Anthony S. Bryken Limba Engleză Hardback – 30 ian 2002
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Specificații
ISBN-13: 9780761919049
ISBN-10: 076191904X
Pagini: 512
Dimensiuni: 152 x 229 x 33 mm
Greutate: 0.81 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Advanced Quantitative Techniques in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 076191904X
Pagini: 512
Dimensiuni: 152 x 229 x 33 mm
Greutate: 0.81 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Advanced Quantitative Techniques in the Social Sciences
Locul publicării:Thousand Oaks, United States
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Recenzii
"The text is authoritative, well laid out, and extremely readable. For the target audience, this book is highly recommended."
"This book is very well written and the applied part is well balanced with technical details. I think that it will be useful not only for social and behavioral researchers but also for applied statisticians, practitioners and students analyzing data with hierarchical-type structures"
"The book is clearly written, well organized, and addresses an important topic. I would recommend this book to the readers of Personnel Psychology. If you want to learn more about these techniques, the new advances, the controversial points, potential links between HLM and meta-analysis, structural equations modeling, item response theory, and so forth , this book is a feast."
"This book makes good use of examples to introduce readers to HLM and the issues surrounding their application. In fact, I think the book does a wonderful job by using lots of examples with lots of details. This is definitely one of its strengths as it makes it much easier for the reader to follow the text and understand the capabilities of the HLM approach. This Second Edition should come highly recommended. I think it gives a very good and thorough overview of HLM, and it does so in a manner that is easy to follow."
"This book is very well written and the applied part is well balanced with technical details. I think that it will be useful not only for social and behavioral researchers but also for applied statisticians, practitioners and students analyzing data with hierarchical-type structures"
"The book is clearly written, well organized, and addresses an important topic. I would recommend this book to the readers of Personnel Psychology. If you want to learn more about these techniques, the new advances, the controversial points, potential links between HLM and meta-analysis, structural equations modeling, item response theory, and so forth , this book is a feast."
"This book makes good use of examples to introduce readers to HLM and the issues surrounding their application. In fact, I think the book does a wonderful job by using lots of examples with lots of details. This is definitely one of its strengths as it makes it much easier for the reader to follow the text and understand the capabilities of the HLM approach. This Second Edition should come highly recommended. I think it gives a very good and thorough overview of HLM, and it does so in a manner that is easy to follow."
Cuprins
PART I THE LOGIC OF HIERARCHICAL LINEAR MODELING
Series Editor 's Introduction to Hierarchical Linear Models
Series Editor 's Introduction to the Second Edition
1.Introduction
2.The Logic of Hierarchical Linear Models
3. Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models
4. An Illustration
PART II BASIC APPLICATIONS
5. Applications in Organizational Research
6. Applications in the Study of Individual Change
7. Applications in Meta-Analysis and Other Cases where Level-1 Variances are Known
8. Three-Level Models
9. Assessing the Adequacy of Hierarchical Models
PART III ADVANCED APPLICATIONS
10. Hierarchical Generalized Linear Models
11. Hierarchical Models for Latent Variables
12. Models for Cross-Classified Random Effects
13. Bayesian Inference for Hierarchical Models
PART IV ESTIMATION THEORY AND COMPUTATIONS
14. Estimation Theory
Summary and Conclusions
References
Index
About the Authors
Series Editor 's Introduction to Hierarchical Linear Models
Series Editor 's Introduction to the Second Edition
1.Introduction
2.The Logic of Hierarchical Linear Models
3. Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models
4. An Illustration
PART II BASIC APPLICATIONS
5. Applications in Organizational Research
6. Applications in the Study of Individual Change
7. Applications in Meta-Analysis and Other Cases where Level-1 Variances are Known
8. Three-Level Models
9. Assessing the Adequacy of Hierarchical Models
PART III ADVANCED APPLICATIONS
10. Hierarchical Generalized Linear Models
11. Hierarchical Models for Latent Variables
12. Models for Cross-Classified Random Effects
13. Bayesian Inference for Hierarchical Models
PART IV ESTIMATION THEORY AND COMPUTATIONS
14. Estimation Theory
Summary and Conclusions
References
Index
About the Authors
Descriere
Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators