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 2 level MLMs Overview of 3 level MLMs Overview of longitudinal designs and their relationships to MLMs Summary 3: Fitting 2-level Models 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 4: 3 Level and Higher Models Defining simple 3-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 5 : Longitudinal Data Analysis using Multilevel Models The Multilevel Longitudinal Framework Person Period Data Structure Fitting Longitudinal Models using the lme4 package Changing the Covariance Structure of Longitudinal Models Benefits of Multilevel Modeling for Longitudinal Analysis Summary 6: Graphing Data in Multilevel Contexts Plots for Linear Models Plotting Nested Data Using the Lattice Package Plotting Model Results using the Effects Package Summary 7: Brief Introduction to Generalized Linear Models Logistic Regression Model for a Dichotomous Outcome Variable Logistic Regression Model for an Ordinal Outcome Variable Multinomial Logistic Regression Models for Count Data Poisson Regression Models for Overdispersed Count data Summary 8: Multilevel Generalized Linear Models (MGLM) MGLMs for a Dichotomous Outcome Variable Random Intercept Logistic Regression Random Coefficient Logistic Regression Inclusion of Additional level 1 and level 2 effects in MGLM MLGM for an Ordinal Outcome Variable Random Intercept Logistic Regression MGLM for Count Data Random Intercept Poisson Regression Random Coefficient Poisson Regression Inclusion of additional level-2 effects to the multilevel Poisson regression model Summary 9: Bayesian Multilevel Modeling MCMCglmm For a Normally Distributed Response Variable Including level-2 Predictors with MCMCglmm User Defined Priors MCMCglmm For a Dichotomous Dependent Variable MCMCglmm for a Count Dependent Variable Summary 10: Advanced Issues in Multilevel Modeling Robust statistics in the multilevel context Identifying potential outliers in single level data Identifying potential outliers in multilevel data Identifying potential multilevel outliers using R Robust and Rank Based Estimation for multilevel models Fitting Robust and Rank Based Multilevel Models in R Multilevel Lasso Fitting the Multilevel Lasso in R Multivariate Multilevel Models Multilevel Generalized Additive Models Fitting GAMM using R Predicting Level-2 Outcomes with Level-1 Variables Power Analysis for Multilevel Models Summary Appendix: An Introduction to R Running Statistical Analyses in R Reading Data into R Missing Data Types of Data Additional R Environment Options amp;lt;/I> Random Intercept Random Slopes Centering Basics of Parameter Estimation with MLMs Maximum Likelihood Estimation Restricted Maximum Likelihood Estimation Assumptions Underlying MLMs Overview of 2 level MLMs Overview of 3 level MLMs Overview of longitudinal designs and their relationships to MLMs Summary 3: Fitting 2-level Models 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 4: 3 Level and Higher Models Defining simple 3-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 5 : Longitudinal Data Analysis using Multilevel Models The Multilevel Longitudinal Framework Person Period Data Structure Fitting Longitudinal Models using the lme4 package Changing the Covariance Structure of Longitudinal Models Benefits of Multilevel Modeling for Longitudinal Analysis Summary 6: Graphing Data in Multilevel Contexts Plots for Linear Models Plotting Nested Data Using the Lattice Package Plotting Model Results using the Effects Package Summary 7: Brief Introduction to Generalized Linear Models Logistic Regression Model for a Dichotomous Outcome Variable Logistic Regression Model for an Ordinal Outcome Variable Multinomial Logistic Regression Models for Count Data Poisson Regression Models for Overdispersed Count data Summary 8: Multilevel Generalized Linear Models (MGLM) MGLMs for a Dichotomous Outcome Variable Random Intercept Logistic Regression Random Coefficient Logistic Regression Inclusion of Additional level 1 and level 2 effects in MGLM MLGM for an Ordinal Outcome Variable Random Intercept Logistic Regression MGLM for Count Data Random Intercept Poisson Regression Random Coefficient Poisson Regression Inclusion of additional level-2 effects to the multilevel Poisson regression model Summary 9: Bayesian Multilevel Modeling MCMCglmm For a Normally Distributed Response Variable Including level-2 Predictors with MCMCglmm User Defined Priors MCMCglmm For a Dichotomous Dependent Variable MCMCglmm for a Count Dependent Variable Summary 10: Advanced Issues in Multilevel Modeling Robust statistics in the multilevel context Identifying potential outliers in single level data Identifying potential outliers in multilevel data Identifying potential multilevel outliers using R Robust and Rank Based Estimation for multilevel models Fitting Robust and Rank Based Multilevel Models in R Multilevel Lasso Fitting the Multilevel Lasso in R Multivariate Multilevel Models Multilevel Generalized Additive Models Fitting GAMM using R Predicting Level-2 Outcomes with Level-1 Variables Power Analysis for Multilevel Models Summary Appendix: An Introduction to R Running Statistical Analyses in R Reading Data into R Missing Data Types of Data Additional R Environment Options amp;lt;P>Comparing Model fit Lme4 and hypothesis testing Summary 4: 3 Level and Higher Models Defining simple 3-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 5 : Longitudinal Data Analysis using Multilevel Models The Multilevel Longitudinal Framework Person Period Data Structure Fitting Longitudinal Models using the lme4 package Changing the Covariance Structure of Longitudinal Models Benefits of Multilevel Modeling for Longitudinal Analysis Summary 6: Graphing Data in Multilevel Contexts Plots for Linear Models Plotting Nested Data Using the Lattice Package Plotting Model Results using the Effects Package Summary 7: Brief Introduction to Generalized Linear Models Logistic Regression Model for a Dichotomous Outcome Variable Logistic Regression Model for an Ordinal Outcome Variable Multinomial Logistic Regression Models for Count Data Poisson Regression Models for Overdispersed Count data Summary 8: Multilevel Generalized Linear Models (MGLM) MGLMs for a Dichotomous Outcome Variable Random Intercept Logistic Regression Random Coefficient Logistic Regression Inclusion of Additional level 1 and level 2 effects in MGLM MLGM for an Ordinal Outcome Variable Random Intercept Logistic Regression MGLM for Count Data Random Intercept Poisson Re.