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

• A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models
• Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations
• Use of the lmer() function in the lme4 R package
• New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs
• Updated graphical procedures in the software packages
• Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options
• More practical recommendations on using the software for analysis
• A new R package (WWGbook) that contains all of the data sets used in the examples

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.

INTRODUCTION
What Are Linear Mixed Models (LMMs)?
A Brief History of Linear Mixed Models

LINEAR MIXED MODELS: AN OVERVIEW
Introduction
Specification of LMMs
The Marginal Linear Model
Estimation in LMMs
Computational Issues
Tools for Model Selection
Model-Building Strategies
Checking Model Assumptions (Diagnostics)
Other Aspects of LMMs
Power Analysis for Linear Mixed Models
Chapter Summary

TWO-LEVEL MODELS FOR CLUSTERED DATA: THE RAT PUP EXAMPLE
Introduction
The Rat Pup Study
Overview of the Rat Pup Data Analysis
Analysis Steps in the Software Procedures
Results of Hypothesis Tests
Comparing Results across the Software Procedures
Interpreting Parameter Estimates in the Final Model
Estimating the Intraclass Correlation Coefficients (ICCs)
Calculating Predicted Values
Diagnostics for the Final Model
Software Notes and Recommendations

THREE-LEVEL MODELS FOR CLUSTERED DATA; THE CLASSROOM EXAMPLE
Introduction
The Classroom Study
Overview of the Classroom Data Analysis
Analysis Steps in the Software Procedures
Results of Hypothesis Tests
Comparing Results across the Software Procedures
Interpreting Parameter Estimates in the Final Model
Estimating the Intraclass Correlation Coefficients (ICCs)
Calculating Predicted Values
Diagnostics for the Final Model
Software Notes
Recommendations

MODELS FOR REPEATED-MEASURES DATA: THE RAT BRAIN EXAMPLE
Introduction
The Rat Brain Study
Overview of the Rat Brain Data Analysis
Analysis Steps in the Software Procedures
Results of Hypothesis Tests           
Comparing Results across the Software Procedures
Interpreting Parameter Estimates in the Final Model
The Implied Marginal Variance-Covariance Matrix for the Final Model
Diagnostics for the Final Model
Software Notes
Other Analytic Approaches
Recommendations

RANDOM COEFFICIENT MODELS FOR LONGITUDINAL DATA: THE AUTISM EXAMPLE
Introduction
The Autism Study
Overview of the Autism Data Analysis
Analysis Steps in the Software Procedures
Results of Hypothesis Tests
Comparing Results across the Software Procedures
Interpreting Parameter Estimates in the Final Model
Calculating Predicted Values
Diagnostics for the Final Model
Software Note: Computational Problems with the D Matrix
An Alternative Approach: Fitting the Marginal Model with an Unstructured Covariance Matrix

MODELS FOR CLUSTERED LONGITUDINAL DATA: THE DENTAL VENEER EXAMPLE
Introduction
The Dental Veneer Study
Overview of the Dental Veneer Data Analysis
Analysis Steps in the Software Procedures
Results of Hypothesis Tests
Comparing Results across the Software Procedures
Interpreting Parameter Estimates in the Final Model
The Implied Marginal Variance-Covariance Matrix for the Final Model
Diagnostics for the Final Model
Software Notes and Recommendations
Other Analytic Approaches

MODELS FOR DATA WITH CROSSED RANDOM FACTORS: THE SAT SCORE EXAMPLE
Introduction
The SAT Score Study
Overview of the SAT Score Data Analysis
Analysis Steps in the Software Procedures
Results of Hypothesis Tests
Comparing Results across the Software Procedures
Interpreting Parameter Estimates in the Final Model
The Implied Marginal Variance-Covariance Matrix for the Final Model
Recommended Diagnostics for the Final Model
Software Notes and Additional Recommendations

APPENDIX A: STATISTICAL SOFTWARE RESOURCES
APPENDIX B: CALCULATION OF THE MARGINAL VARIANCE-COVARIANCE MATRIX
APPENDIX C: ACRONYMS/ABBREVIATIONS

BIBLIOGRAPHY

INDEX