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Snijders, Tom A.B., and Bosker, Roel J.
Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, second edition.
London etc.: Sage Publishers, 2012

ISBN 9781849202008 (hardcover), ISBN 9781849202015 (pbk). xii + 368 p.

The second edition of the textbook,
Multilevel Analysis: An introduction to basic and advanced multilevel modeling,
written by Tom A.B. Snijders and Roel J. Bosker, appeared November 2011 at Sage Publishers.
The official publication year, however, is 2012.

The Sage announcement of this book is here, and here is the table of contents.

The book was totally updated compared to the first edition, with new chapters on missing data, survey weights in multilevel analysis, and miscellaneous methods (Bayesian estimation, sandwich standard errors, latent class models).

Each chapter (from 2 to 17) ends with a glommary, which is a combination of a glossary and a summary, giving the main terms and an overview of the chapter.

This webpage contains:

  1. Data sets used in the book.
  2. Macros / scripts that may be useful in general.
  3. Software setups for reproducing examples in the book.
  4. Additional remarks and references.
  5. Corrections.

Data Sets

Macros / scripts

The following macros/scripts are used in the software setups below, and can be helpful for data analysis in general.





Software Setups

These software scripts, macros, etc., use the data sets given above.
Thanks to Jon Fahlander and Tim Mueller for contributing the Stata do-files.

Note that you can download the files below in many browsers by right-clicking on the file, and choosing something like "save as".

Chapter Title
Chapter 3     CH3ex7.obe         Statistical Treatment of Clustered Data
Chapter 4    CH4568.obe   ch45.r The Random Intercept Model
Chapter 5    CH4568.obe   ch45.r The Hierarchical Linear Model
Chapter 6    CH4568.obe   ch6.r Testing and Model Specification
Chapter 7             How much does the model explain?
Chapter 8    CH4568.obe   ch8.r Heteroscedasticity
Chapter 9       ch9.r Missing Data
Chapter 10     ch10.obe
  ch10.r Assumptions of the Hierarchical Linear Model
Chapter 11             Designing Multilevel Studies
Chapter 12   Other Models and Methods
Chapter 13   Imperfect Hierarchies
Chapter 14     PISA.obe   pisa_b.R Survey Weights
Chapter 15    soep5560_21.obe   ch_15.r       Longitudinal Data
Chapter 16     CH16.obe   ch16.r       Multivariate Multilevel Models
Chapter 17       ch17.r
 ch17_ex6.r Discrete Dependent Variables

Additional remarks and references


Materials Oxford Spring School, April 2012

Back to the multilevel page of Tom Snijders