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

1. Introduction
2. Linear Mixed Models: An Overview
3. Two-Level Models for Clustered Data: The Rat Pup Example
4. Three-Level Models for Clustered Data
5. Models for Repeated-Measures Data: The Rat Brain Example
6. Random Coe¿cient Models for Longitudinal Data: The Autism Example
7. Models for Clustered Longitudinal Data: The Dental Veneer Example
8. Models for Data with Crossed Random Factors: The SAT Score Example
9. Power Analysis and Sample Size Calculations for Linear Mixed Models
A. Statistical Software Resources
B. Calculation of the Marginal Covariance Matrix
C. Acronyms / Abbreviations