Average Reviews:
(More customer reviews)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.
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The Second Edition of this classic text introduces the main methods, techniques, and issues involved in carrying out multilevel modeling and analysis. Snijders and Boskers' book is an applied, authoritative, and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book has been comprehensively revised and updated since the last edition, and now includes guides to modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and Mix.
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