Linear Mixed Models: A Practical Guide Using Statistical Software, 3rd Edition

English | 2022 | ISBN: 978-1032019321 | 490 Pages | PDF, EPUB | 53 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.