Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Authors -- 1: Linear Models -- Simple Linear Regression -- Estimating Regression Models with Ordinary Least Squares -- Distributional Assumptions Underlying Regression -- Coefficient of Determination -- Inference for Regression Parameters -- Multiple Regression -- Example of Simple Linear Regression by Hand -- Regression in R -- Interaction Terms in Regression -- Categorical Independent Variables -- Checking Regression Assumptions with R -- Summary -- 2: An Introduction to Multilevel Data Structure -- Nested Data and Cluster Sampling Designs -- Intraclass Correlation -- Pitfalls of Ignoring Multilevel Data Structure -- Multilevel Linear Models -- Random Intercept -- Random Slopes -- Centering -- Basics of Parameter Estimation with MLMs -- Maximum Likelihood Estimation -- Restricted Maximum Likelihood Estimation -- Assumptions Underlying MLMs -- Overview of Two-Level MLMs -- Overview of Three-Level MLMs -- Overview of Longitudinal Designs and Their Relationship to MLMs -- Summary -- 3: Fitting Two-Level Models in R -- Simple (Intercept-Only) Multilevel Models -- Interactions and Cross-Level Interactions Using R -- Random Coefficients Models using R -- Centering Predictors -- Additional Options -- Parameter Estimation Method -- Estimation Controls -- Comparing Model Fit -- lme4 and Hypothesis Testing -- Summary -- Note -- 4: Three-Level and Higher Models -- Defining Simple Three-Level Models Using the lme4 Package -- Defining Simple Models with More than Three Levels in the lme4 Package -- Random Coefficients Models with Three or More Levels in the lme4 Package -- Summary -- Note -- 5: Longitudinal Data Analysis Using Multilevel Models -- The Multilevel Longitudinal Framework -- Person Period Data Structure -- Fitting Longitudinal Models Using the lme4 Package.