Linear Mixed Models: A Practical Guide Using Statistical Software

Brady T. West, Kathleen B. Welch, Andrzej T Galecki

November 22, 2006 by Chapman and Hall/CRC
Reference - 374 Pages - 38 B/W Illustrations
ISBN 9781584884804 - CAT# C4800

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  • 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
  • Supplies software code in each chapter to compare the relative advantages and disadvantages of each package
  • Includes a supporting website that features many of the data sets used in the examples as well as the most up-to-date versions of selected portions of the syntax associated with the examples
  • Summary

    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.