Hardcover. 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 dataProvides descriptions, explanations, and examples of software code necessary to fit LMMs in SAS, SPSS, R, Stata, and HLMContains detailed tables of estimates and results, allowing for easy comparisons across software proceduresPresents 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 diagnosticsIntegrates software code in each chapter to compare the relative advantages and disadvantages of each packageSupplemented by a website with software code, datasets, additional documents, and updatesIdeal 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. 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. There is a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: Linear Mixed Models (Hardcover)
Publisher: Taylor & Francis Ltd, London
Publication Date: 2022
Language: English
ISBN 10: 1032019328
ISBN 13: 9781032019321
Binding: Hardcover
Edition: 3rd Edition
Condition: new

About this title

Synopsis

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.

About the Author

Brady T. West

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