Multilevel Analysis: Techniques and Applications, Second Edition (Quantitative Methodology Series) Review

Multilevel Analysis: Techniques and Applications, Second Edition (Quantitative Methodology Series)
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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.

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This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions, making it useful as both an introduction and as a reference to students, researchers, and methodologists. Basic models and examples are discussed in non-technical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines. For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epilepsy, divorce, and sociometric scores, to name a few. The data sets are available on the website in SPSS, HLM, MLwiN, LISREL and/or Mplus files. Readers are introduced to both the multilevel regression model and multilevel structural models.Highlights of the second edition include:Two new chapters-one on multilevel models for ordinal and count data (Ch. 7) and another on multilevel survival analysis (Ch. 8).Thoroughly updated chapters on multilevel structural equation modeling that reflect the enormous technical progress of the last few years.The addition of some simpler examples to help the novice, whilst the more complex examples that combine more than one problem have been retained.A new section on multivariate meta-analysis (Ch. 11).Expanded discussions of covariance structures across time and analyzing longitudinal data where no trend is expected.Expanded chapter on the logistic model for dichotomous data and proportions with new estimation methods.An updated website at http://www.joophox.net/ with data sets for all the text examples and up-to-date screen shots and PowerPoint slides for instructors.Ideal for introductory courses on multilevel modeling and/or ones that introduce this topic in some detail taught in a variety of disciplines including: psychology, education, sociology, the health sciences, and business. The advanced extensions also make this a favorite resource for researchers and methodologists in these disciplines. A basic understanding of ANOVA and multiple regression is assumed. The section on multilevel structural equation models assumes a basic understanding of SEM.

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