TUM Library

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 CorrelationPitfalls 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 PredictorsAdditional 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)

MGLMs for a Dichotomous Outcome VariableRandom Intercept Logistic Regression; Random Coefficients Logistic Regression; Inclusion of Additional Level-1 and Level-2 Effects in MGLM; MGLM 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

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. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. 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. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT, Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.

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