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Multilevel Analysis
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Produktbeschreibungen
Pressestimmen
"The author has chosen a technical level for the presentation that allows precision in the model specifications and the discussion of more advanced issues without detracting from its applied focus. This book provides a good reference for those who want to grasp the essentials of this widely used methodology."
—Short Book Reviews
"...provides an intuitive understanding of the topic while building on a basic foundation of classical multiple regression....is very well organized and is a nice resource to supplement other texts....I especially recommend this book to practitioners and consultants who work in social-science-related disciplines, although it is also handy for anyone that works with mixed models."
—Journal of the American Statistical Association
Kurzbeschreibung
This volume provides an introduction to multilevel analysis for applied researchers. The term "multilevel" refers to a hierarchical or nested data structure, usually individuals within organizational groups, but the nesting may also consist of repeated measurements on individuals over time or individuals within clusters as in cluster sampling. The expression multilevel model or multilevel analysis is used as a generic term for all models for hierarchical or nested data. This book presents two types of multilevel models: the multilevel regression model; and a model for multilevel covariance structures. Despite the book being an introduction, it includes a discussion of many extensions and special applications.
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10 von 10 Kunden fanden die folgende Rezension hilfreich
One marvelous page ... and a good book
12. Juni 2009
Format:Taschenbuch|Von Amazon bestätigter Kauf
Hox provides a good, conventional tratement of multilevel modeling, one that is much better than De Leeuw's on-line review would have the reader suspect. When struggling with this material for the first time, moreover, Hox's one-page treatment of models with more than two levels is worth the price of the book. His cautionary comments alert the reader to the sometimes intractable complexity that may be occasioned by even three-level models, much less four or more.
Kelvyn Jones takes issue online with this admonition, and, no doubt, there are informative three level models. But Hox's observation is still eminently applicable. In my experience, the amount of work required to make the transition to three-level models is underestimated in most textbook accounts.
Part of the problem inheres in making more and more difficult specification decisions in the absence of readily interpretable guidance from theoretical and substantive literature. Beyond that, models with three or more levels quickly become statistically very complex. The number of random component variances and covariances increases dramatically with he addition of predictors with random slopes. Parallels between two-level and three-level models are a good deal less obvious when it comes to actually specifying three-level models. Model building facility takes practice.
In spite of all this, three-level models can be useful, providing insights that otherwise would not be available. However, off-handed assumptions that three-level regression models are just straightforward extensions of two-level models may lead us to expect too much. Three-level models are uniquely complex, and their effective application demands more theoretical and substantive knowledge than is typically available.
OK, Hox's one-page warning did not contain all this material, certainly not enough information to actually buy the book for just one cautionary page. Nevertheless, until I stumbled on that page, I struggled more with, and gave much more attention to models with more than two levels than they usually deserve.
Another real virtue of Hox's book is that, in contrast to most other texts dealing with multilevel models, it gives adequate attention to the really interesting topic of constructing intervals for random intercepts and slopes, providing estimates of how much they vary group to group. In some instances, the degree of variability is startlingly large, making clear that fixed components, as usually reported, can be very misleading.
For most readers, Hox's book is not easy, but it's clear that the author understands that the complexity of the material will make it difficult for most of us to quickly grasp. It is obvious from the patient, largely non-mathematical nature of his presentation that he wants folks who have paid for his book to benefit from an investment of time and effort in understanding multilevel modeling. He does this, moreover, while covering a broader range of topics than most texts of this kind.
All tolled, Hox's book certainly deserves the four stars I've given it. Another edition is scheduled to be published in 2010, and it deserves a look.
Kelvyn Jones takes issue online with this admonition, and, no doubt, there are informative three level models. But Hox's observation is still eminently applicable. In my experience, the amount of work required to make the transition to three-level models is underestimated in most textbook accounts.
Part of the problem inheres in making more and more difficult specification decisions in the absence of readily interpretable guidance from theoretical and substantive literature. Beyond that, models with three or more levels quickly become statistically very complex. The number of random component variances and covariances increases dramatically with he addition of predictors with random slopes. Parallels between two-level and three-level models are a good deal less obvious when it comes to actually specifying three-level models. Model building facility takes practice.
In spite of all this, three-level models can be useful, providing insights that otherwise would not be available. However, off-handed assumptions that three-level regression models are just straightforward extensions of two-level models may lead us to expect too much. Three-level models are uniquely complex, and their effective application demands more theoretical and substantive knowledge than is typically available.
OK, Hox's one-page warning did not contain all this material, certainly not enough information to actually buy the book for just one cautionary page. Nevertheless, until I stumbled on that page, I struggled more with, and gave much more attention to models with more than two levels than they usually deserve.
Another real virtue of Hox's book is that, in contrast to most other texts dealing with multilevel models, it gives adequate attention to the really interesting topic of constructing intervals for random intercepts and slopes, providing estimates of how much they vary group to group. In some instances, the degree of variability is startlingly large, making clear that fixed components, as usually reported, can be very misleading.
For most readers, Hox's book is not easy, but it's clear that the author understands that the complexity of the material will make it difficult for most of us to quickly grasp. It is obvious from the patient, largely non-mathematical nature of his presentation that he wants folks who have paid for his book to benefit from an investment of time and effort in understanding multilevel modeling. He does this, moreover, while covering a broader range of topics than most texts of this kind.
All tolled, Hox's book certainly deserves the four stars I've given it. Another edition is scheduled to be published in 2010, and it deserves a look.
1 von 1 Kunden fanden die folgende Rezension hilfreich
On-line Data does not currently exist.
12. August 2011
Format:Taschenbuch|Von Amazon bestätigter Kauf
Overall this is an excellent text for understanding and conducting mulitlevel analysis. Hox makes the promise that the data sets used in the book would be made available online. At the time of this review Aug 12 2011 the data is not available.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Accessible and user oriented
28. Februar 2011
Format:Taschenbuch|Von Amazon bestätigter Kauf
Very useful text and has helped me move forward in my research. It situates the multilevel analysis procedure in the continuously evolving context of multivariate analysis and outlines some of the major parameters for conceptualizing and building your own models. The text is very accessible for someone who is considering multilevel modeling and has some basic background in regression analysis and other multivariate techniques. It is written in a very non-technical and non-algebraic language with numerous examples.
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