The main methods, techniques and issues for carrying out multilevel modeling and analysis are covered in this book. The book is an applied introduction to the topic, providing a clear conceptual understanding of the issues involved in multilevel analysis and will be a useful reference tool. Information on designing multilevel studies, sampling, testing and model specification and interpretation of models is provided. A comprehensive guide to the software available is included. Multilevel Analysis is the ideal guide for researchers and applied statisticians in the social sciences, including education, but will also interest researchers in economics, and biological, medical and health disciplines.
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9 of 10 people found the following review helpful:
4.0 out of 5 stars
Difficult but useful as a reference for the experienced multilevel analyst,
By not a natural "Bob Bickel" (huntington, west virginia United States) - See all my reviews (VINE VOICE)
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This review is from: Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (Paperback)
Snijders and Bosker's Multilevel Analysis is mathematically demanding but more readable than Raudenbush and Bryk's Hierarchical Linear Models. Snijders and Bosker's text would be much more useful for the less-experienced reader if it contained a directory identifying essential material. As is often the case with books of this kind, it is difficult to distinguish between really important topics, those crucial to understanding multilevel models, and ancillary topics which can be treated as non-essential, at least for the first pass through this dense and difficult material.It would also help if the authors located multilevel modeling within a statistical context likely to contain material that readers already understand. As it is, multilevel analysis is treated as a separate and new topic, and is readily accessible only to those with generalized mathematical maturity. The authors do, however, cover a broad range of pertinent material. Thus, while it is not a good choice for beginners looking for a self-instructional tool, the statistically sophisticated reader will find it to be an excellent reference. While just as difficult to understand as the rest of the book, Snijders and Bosker's develop some really informative and interesting examples of three-level models. This text has been in print for a decade, and is still widely used. I've found that as I become more familiar with multilevel modeling, the book becomes more valuable.
1 of 4 people found the following review helpful:
5.0 out of 5 stars
Good reference,
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This review is from: Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (Paperback)
Was a requirement for a multi-level modeling course. Great resource to have. Everyone uses it and it's a must.
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Inside This Book
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First Sentence:
Multilevel analysis is a methodology for the analysis of data with complex patterns of variability, with a focus on nested sources of variability: e.g., pupils in classes, employees in firms, suspects tried by judges in courts, animals in litters, longitudinal measurements of subjects, etc. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
random slope variance, residual intraclass, fixed occasion design, random slope models, random intercept model, total regression coefficient, intercept variance, compound symmetry model, crossed random effects, random slopes, multilevel software, random group effects, psychological test theory, multivariate residual, cohabitation data, empty model, hierarchical linear model, deviance tests, slope variances, probit regression model, multilevel data, random part, heteroscedastic model, language test score, crossed effects Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fixed Effect Coefficient, Random Effect Parameter, Random Effect Variance Component, Fixed Effect Par, Fixed Effects Coefficient, Random Effects Var New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Multilevel analysis is a methodology for the analysis of data with complex patterns of variability, with a focus on nested sources of variability: e.g., pupils in classes, employees in firms, suspects tried by judges in courts, animals in litters, longitudinal measurements of subjects, etc. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
random slope variance, residual intraclass, fixed occasion design, random slope models, random intercept model, total regression coefficient, intercept variance, compound symmetry model, crossed random effects, random slopes, multilevel software, random group effects, psychological test theory, multivariate residual, cohabitation data, empty model, hierarchical linear model, deviance tests, slope variances, probit regression model, multilevel data, random part, heteroscedastic model, language test score, crossed effects Key Phrases - Capitalized Phrases (CAPs): (learn more)
Fixed Effect Coefficient, Random Effect Parameter, Random Effect Variance Component, Fixed Effect Par, Fixed Effects Coefficient, Random Effects Var New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 31
books:
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19
books
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- Residuals and Influence in Regression (Monographs on Statistics and Applied Probability, 18) by R. Dennis Cook in Back Matter
- Statistical Methods in Education and Psychology (3rd Edition) by Gene V. Glass in Back Matter
- Discrete Multivariate Analysis: Theory and Practice by Yvonne M. Bishop in Back Matter
- Sampling Techniques, 3rd Edition by William G. Cochran in Back Matter
- An Introduction to Regression Graphics (Wiley Series in Probability and Statistics) by R. Dennis Cook in Back Matter
See all 31 books this book cites
- Statistical Group Comparison (Wiley Series in Probability and Statistics) by Tim Futing Liao on page 175, and Back Matter
- Geodemographics, GIS and Neighbourhood Targeting (Mastering GIS: Technol, Applications & Mgmnt) by Richard Harris in Back Matter
- Geodemographics, GIS and Neighbourhood Targeting (Mastering GIS: Technol, Applications & Mgmnt) by Richard Harris in Back Matter
- Neighborhoods and Health (Medicine) by Ichiro Kawachi on page 109
- Contemporary Multivariate Analysis And Design of Experiments (Series in Biostatistics) by Jianqing Fan on page 119
See all 19 books citing this book
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