Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling

by Tom A.B. Snijders, Roel Bosker

The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis.

Snijders and Bosker′s 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 provides step-by-step coverage

• multilevel theories

• ecological fallacies

• the hierarchical linear model

• testing and model specification

• heteroscedasticity

• study designs

• longitudinal data

• multivariate multilevel models

• discrete dependent variables

There are also new chapters

• missing data

• multilevel modeling and survey weights

• Bayesian and MCMC estimation and latent-class models.

This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix.

This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis.

Tom A.B. Snijders is Professor of Statistics in the Social Sciences at the University of Oxford and Professor of Statistics and Methodology at the University of Groningen.

Roel J. Bosker is Professor of Education and Director of GION, Groningen Institute for Educational Research, at the University of Groningen.

Genres: Reference

368 pages, Kindle Edition

First published January 1, 1999

Reviews

[Christophe Van · Rating 5 out of 5]

I read the first edition about 15 years ago when trying to learn linear regression with random effects on my own. I first read Hierarchical Linear Models: Applications and Data Analysis Methods, which easier to understand. I think reading the book by Snijders and Bosker only after going through that other simpler book helped me pick up more from the second, somewhat more advanced book. I recommend both books enthusiastically, but consider the book bij Snijders and Bisker best used as a next-level resource rather than as a starting point for self-learners.

[szymborskalyte · Rating 5 out of 5]

Textbooks really don’t get any better than this.

[Ashley Hunter · Rating 5 out of 5]

Easy to read for someone teaching themselves and who already has an understanding of multiple regression

[Cat · Rating 3 out of 5]

As an introduction to the topic, it was serviceable. Several times I was frustrated by its vagueness-the notation for some of the equations is confusing, and the authors would have done well to introduce their notation a little better. I also found their occasional use of Latin phrases frustrating; there was no reason to use phrases like "inter alia" when "among other things" would have sufficed.

On the other hand, its one of the only books on the topic, and it hits most of the theory necessary. I found Multilevel and Longitudinal Modeling with IBM SPSS to be a necessary companion; it has much less theory and clearly explains the interpretation of SPSS results.

[Katie · Rating 4 out of 5]

After reading this, I think I'll be able to actually perform the analysis. That being said, it was a tough read. But that's mostly due to the content, not the writing. I think it's a good textbook for statisticians - not for beginners (despite the author's claim that you don't need to have prior knowledge of statistics to read it).

[Abrar · Rating 3 out of 5]

A basic book of multilevel modeling and advanced statistics , it was helpful for those who want to study different model effects.