Linear Mixed Models: A Practical Guide Using Statistical Software is published by Chapman and Hall/CRC in July 2014. This book has 440 pages in English, ISBN-13 978-1466560994.
Highly recommended by JASA, Technometrics, and other journals, the first edition 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, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM.
New to the Second Edition
- A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models
- Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations
- Use of the lmer() function in the lme4 R package
- New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs
- Updated graphical procedures in the software packages
- Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options
- More practical recommendations on using the software for analysis
- A new R package (WWGbook) that contains all of the data sets used in the examples
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.