Introduction to Mixed Modelling (PDF)

Mixed modelling is very useful, and easier than you
think!

Mixed modelling is now well established as a powerful approach
to statistical data analysis. It is based on the recognition of
random-effect terms in statistical models, leading to inferences
and estimates that have much wider applicability and are more
realistic than those otherwise obtained.

Introduction to Mixed Modelling leads the reader
into mixed modelling as a natural extension of two more familiar
methods, regression analysis and analysis of variance. It provides
practical guidance combined with a clear explanation of the
underlying concepts.

Like the first edition, this new edition shows diverse
applications of mixed models, provides guidance on the
identification of random-effect terms, and explains how to obtain
and interpret best linear unbiased predictors (BLUPs).
It also introduces several important new topics, including the
following:
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* Use of the software SAS, in addition to GenStat and R.

* Meta-analysis and the multiple testing problem.

* The Bayesian interpretation of mixed models.

Including numerous practical exercises with solutions, this book
provides an ideal introduction to mixed modelling for final year
undergraduate students, postgraduate students and professional
researchers. It will appeal to readers from a wide range of
scientific disciplines including statistics, biology,
bioinformatics, medicine, agriculture, engineering, economics,
archaeology and geography.

Praise for the first edition:

"One of the main strengths of the text is the bridge it
provides between traditional analysis of variance and regression
models and the more recently developed class of mixed models...Each
chapter is well-motivated by at least one carefully chosen
example...demonstrating the broad applicability of mixed models in
many different disciplines...most readers will likely learn
something new, and those previously unfamiliar with mixed models
will obtain a solid foundation on this
topic."--Kerrie Nelson University of
South Carolina, in American Statistician, 2007