Introduction. Linear Mixed Models: An Overview. Two-Level Models for Clustered Data: The Rat Pup Example. Three-Level Models for Clustered Data: The Classroom Example. Models for Repeated-Measures Data: The Rat Brain Example. Random Coefficient Models for Longitudinal Data: The Autism Example. Models for Clustered Longitudinal Data: The Dental Veneer Example. Models for Data with Crossed Random Factors: The SAT Score Example. Appendices. Bibliography. Index.
"I can recommend the book even for owners of the first edition. … I
can highly recommend this book for all readers who are interested
in having a quick reference guide for various procedures regarding
LMMs at hand. It covers a lot of different situations in which LMMs
are applied and it shows the use of the most commonly used software
packages in all of these different settings."
—Biometrical Journal, 2015"… a broad and accessible reference of
linear models. The authors did an excellent job making substantial
revisions in this edition. … The literature review is relevant and
thorough for low-dimensional cases. The chapters are nicely
structured, well presented, and motivated. … The main strength of
the book is that it offers many numerical examples that are based
on real data sets emerging from a host of research. … equally
attractive to practitioners, graduate students, and
researchers."
—Technometrics, February 2015Praise for the First Edition:"… a good
overview of the main types or variants of LMMs available.
Furthermore, the book also gives a very well-balanced treatment to
five mainstream software packages used to fit the LMMs used in each
example. The treatment of the examples is done in a rather thorough
way, very useful for practitioners. This book is thus highly
recommended to all those who are mainly interested in learning how
to fit a specific LMM to their data or willing to learn what kinds
of data and for what kind of situations LMMs may be fit and
adequate. It may also be a very good starting point for those
willing to get a more in-depth knowledge of LMMs … a very useful
manual for the application of LMMs, which will contribute beyond
any doubt to the development of work in this and related
areas."
—Carlos A. Coelho and Abel M. Rodrigues, Journal of Statistical
Theory and Practice, 2012"In this book the authors take on the
herculean task of demonstrating how to perform complex LMM analyses
with five programs: HLM, R/S–PLUS, SAS, SPSS, and Stata. It is much
more than a software manual; through the use of excellent
introductory material and details given throughout, it provides a
solid introduction to LMM analysis. … Software-oriented books can
quickly become dated, but the [book’s] website
http://www-personal.umich.edu/~bwest/almmussp.html appears to keep
up with new developments … this book is a tremendous contribution
to the field of applied mixed modeling. It is much more than a
software manual. It is well organized, has minimal typographical
errors, and contains a complete index. It could easily serve as a
reference guide. Anyone working with LMMs should seriously consider
obtaining this book."
—Technometrics, May 2009, Vol. 51, No. 2"… the book is very useful
for the well-informed practitioner, who wants to fit LMMs and needs
to make a choice about the specific statistical software to
use."
—Biometrical Journal, Vol. 51, 2009"… useful to someone who wanted
to understand the process of setting up, conducting, and evaluating
a mixed model analysis. West et al. [is] appropriate for a
researcher with problems where model selection is a major component
of the analysis. ... would also be useful to a practicing
statistician who is familiar with mixed models and wishes to use a
new statistical package."
—Biometrics, December 2008"This text is a most welcome addition to
the literature on regression models. … It is one of those rare
texts with no glaring omissions or obvious shortcomings. The book
consolidates syntax germane to LMMs for most major software
packages, obviating the need to consult multiple platform-specific
texts. Most importantly, the material is presented in an
easy-to-read, sensibly organized fashion … a must buy for the
applied statistician and researcher alike."
—Gregory E. Gilbert, Journal of the American Statistical
Association"I commend this book to anyone who is using software for
statistical modelling, either for a detailed account of specific
linear models or an exemplar of how to gather the information to
compare software."
—Journal of the Royal Statistical Society"… a good reference for
any practicing statisticians and researchers who want a basic
introduction to the topic ... also useful for researchers who need
to compare their analysis to existing works done using different
software packages. Because the basic concept is well summarized and
presented through examples and tables ... I would recommend this
textbook as a special topic for teaching an advanced undergraduate
or introductory graduate course on linear models."
—Journal of Quality Technology"... an excellent first course in the
theory and methods of linear mixed models … also provides a
thorough and up-to-date guide through the major software
applications for linear mixed models, namely, Stata, SAS, R, SPSS,
and HLM. Each of five middle chapters highlights a different
software package and teaches you the basics of fitting mixed models
therein. Tables comparing each package show the results obtained
from fitting identical models, ... . If you wish to fit linear
mixed models, whether in Stata or elsewhere, we recommend this
text."
—Stata Technical Group
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