Highly recommended by JASA, Technometrics, and other leading
statistical journals, the first two editions of this bestseller
showed how to easily perform complex linear mixed model (LMM)
analyses via a variety of software programs. Linear Mixed Models: A
Practical Guide Using Statistical Software, Third Edition continues
to lead readers step-by-step through the process of fitting LMMs.
The third edition provides a comprehensive update of the available
tools for fitting linear mixed-effects models in the newest
versions of SAS, SPSS, R, Stata, and HLM. All examples have been
updated, with a focus on new tools for visualization of results and
interpretation. New conceptual and theoretical developments in
mixed-effects modeling have been included, and there is a new
chapter on power analysis for mixed-effects models.
Features:*Dedicates an entire chapter to the key theories
underlying LMMs for clustered, longitudinal, and repeated measures
data *Provides descriptions, explanations, and examples of software
code necessary to fit LMMs in SAS, SPSS, R, Stata, and HLM
*Contains detailed tables of estimates and results, allowing for
easy comparisons across software procedures *Presents step-by-step
analyses of real-world data sets that arise from a variety of
research settings and study designs, including hypothesis testing,
interpretation of results, and model diagnostics *Integrates
software code in each chapter to compare the relative advantages
and disadvantages of each package *Supplemented by a website with
software code, datasets, additional documents, and updates Ideal
for anyone who uses software for statistical modeling, this book
eliminates the need to read multiple software-specific texts by
covering the most popular software programs for fitting LMMs in one
handy guide. The authors illustrate the models and methods through
real-world examples that enable comparisons of model-fitting
options and results across the software procedures.
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