Linear Mixed Models

West, Brady T.

Welch, Kathleen B.

Galecki, Andrzej T

Boca Raton : C&H CRC Press

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.

Business & Money

Education & Reference

Chapman and Hall/CRC

Highly recommended by JASA, Technometrics, and other journals, the first edition 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, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition <ul>
<li>A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models </li> <li>Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations</li> <li>Use of the lmer() function in the lme4 R package </li> <li>New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs</li> <li>Updated graphical procedures in the software packages</li> <li>Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options</li> <li>More practical recommendations on using the software for analysis</li> <li>A new R package (WWGbook) that contains all of the data sets used in the examples</li> </ul> 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.

Science & Math

Mathematics

Routledge

Take the guesswork out of selecting a statistical technique for social science data! This guide will help you select from the vast array of statistical techniques that can be applied in a particular analysis. It addresses social scientists, data analysts, and graduate students who have some knowledge of social science statistics and who want a systematic, highly condensed overview of many of the statistical techniques in current use and the purposes for which each is intended. Originally published in 1971, this guide has been updated to incorporate current statistical and analytical developments. In addition, you'll find a summary of how each of the techniques is provided through SAS software.

Selecting Statistical Techniques for Social Science Data : A Guide for SAS

Computers & Technology

Computer Science

SAS Inst

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

Software

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