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Free Download Linear Mixed Models: A Practical Guide Using Statistical Software: Third Edition
by Brady T. West
English | 2022 | ISBN: 1032019328 | 490 pages | True PDF | 30.12 MB
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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