Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels.
Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. The applications and exercises span a wide range of disciplines, making the book suitable for courses on multilevel and longitudinal modeling in the medical, social, and behavioral sciences and in applied statistics. This extensively revised second edition includes 3 new chapters, comprehensive updates for Stata 10, 38 new exercises, and 27 new data sets.
The authors teach multilevel and longitudinal modeling at their universities and frequently hold workshops at international conferences. They have been developing a general modeling framework, GLLAMM, and Stata software gllamm for multilevel and latent variable modeling. This work has been published in their highly acclaimed book Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models and in many journals, including Biometrics, Psychometrika, Journal of Econometrics, and Journal of the Royal Statistical Society.
"... a considerably expanded version, nearly double the size of the original. Much of the added material serves to delineate more clearly between statistics and software. ... throughout the book, separate sections and subsections entitled "Estimation with Stata" help to separate the discussion of the models from the discussion of the fitting of the models using Stata. This improves the readability of the book and opens it up to a potentially broader audience."
―Biometrics, December 2008
"... I will replace my first edition with this one and keep it ... on my shelf as a reference. I also envision using it as a primary text for a longitudinal regression models course at the advanced undergraduate or master’s level. Finally, I can imagine using it as a tutorial in regression modeling using Stata and using it as an accessible introduction to more advanced methods. The authors have provided a well-rounded and complete approach to model-fitting and interpretation of an important family of models. Once again, they are to be commended for helping foster the appropriate use of these regression models."
―The Stata Journal, 2008
Praise for the First Edition
“All too often computer manuals leave off ... important aspects of an analysis, but the authors have been careful to provide a well-rounded and complete approach to model fitting and interpretation.”
―American Statistician, August 2006
“This is a useful reference source for researchers involved with multilevel modeling. It gives a fairly comprehensive treatment of methods for analysis of multilevel data, with a particular focus on random effects models. Rabe-Hesketh and Skrondal’s work would also be quite helpful as an adjunct text for courses on multilevel modeling. It could serve as a stand-alone text for courses that focus on applications and implementation of the methods... . One of the appealing features of the book is the use of interesting data sets throughout to illustrate the application of the methods. In addition to the data sets used in the text, many more data sets form the bases of interesting exercises provided after each chapter. All of the data sets can be freely downloaded from a website provided by the authors. Another useful feature is the detailed Stata commands for all the results presented, which will allow the reader to easily conduct the analyses on their own data sets. A strength of the book is the clear and detailed explanations of how to interpret all the models presented; the graphical depictions of the models are particularly helpful in this regard. ...”
―Brian Leroux (University of Washington), Statistics in Medicine, July 2008
