Linear Mixed Models: A Practical Guide Using Statistical Software

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Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on both general and hierarchical model specifications, develop the model-building process step-by-step, and demonstrate the estimation, testing, and interpretation of fixed-effect parameters and covariance parameters associated with random effects. These concepts are illustrated through examples using real-world data sets that enable comparisons of model fitting options and results across the software procedures. The book also gives an overview of important options and features available in each procedure. Making popular software procedures for fitting LMMs easy-to-use, this valuable resource shows how to perform LMM analyses and provides a clear explanation of mixed modeling techniques and theories.

Linear Mixed Models: A Practical Guide Using Statistical Software Review

This is a very unusual and useful book. Whereas most statistics books have a programming section as the first or second chapter or as an appendix, this book has an opening chapter which very briefly explains the concepts of linear mixed models (aka hierarchical linear models or mixed effects models) and then a long chapter explaining the math. The rest of the book has chapters that are case studies in analyzing data with all the major packages (Stata, SAS, R, SPSS, and HLM). Each analysis chapter focuses on one language but there is coverage with the others in the book and on the book's website.

The case study chapters cover:
Two level clustered data - assessing children who are in classrooms
Three level clustered data - children in classrooms in districts
Repeated measures - different parts of rat brains
Longitudinal data - development of autistic children
Clustered longitudinal data - teeth in subjects over time

These case studies go far beyond a cookbook explaining what each option in the code does. They show model building, complete with strategies for which models to compare and they use well annotated code. For the languages I know the code is not overly simple or complex (unfortunately the SAS code in the book and website is for 9.1.3 not 9.2 graphics).

The authors assume the reader has a background in linear algebra (especially) for the 2nd chapter but the rest of the book does not require a working understanding of advanced math. While the writing is clear, this chapter will be a significant impediment to anyone without a degree in math or statistics. For example, the authors talk about issues like the importance of positive definite matrices but never explain what they are or why anyone would care.

A weakness of this book is in the design. Because the authors are working with many programming languages they separate the analysis plan from the actual code. This leave the reader flipping back and forth constantly between sections to understand how each block of code relates to the analysis plan. The reader can figure out why each analysis is being done but it is taxing.

Overall this is a very good second book on mixed effects models and if you need to do an analysis like the case studies above you definitely want to get a copy.

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