Multilevel modeling using R

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.

Benefits of Using Multilevel Modeling for Longitudinal AnalysisSummary; Note; 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 (MGLMs)

Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.

"This book presents the theory and practice of major multilevel modeling techniques in a variety of contexts using R as the software tool. It describes the applications and extensions of multilevel modeling with special emphasis on the use of R to conduct the analyses and interpret the resulting output. The book is designed for researchers, data analysts, and upper-level undergraduate and graduate students taking a course on multilevel modeling or statistical modeling. "--