Description
Taylor and Francis Multilevel Modeling Using R 2Nd Edition 2019 Edition by W. Holmes Finch, Jocelyn E. Bolin, Ken Kelley
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. 1: Linear Models Simple Linear Regression Estimating Regression Models with Ordinary Least Squares Distributional Assumptions Underlying Regression Coefficient of DeterminationInference for Regression Parameters Multiple Regression Example of Simple Linear Regression by Hand Regression in R Interaction Terms in Regression Categorical Independent VariablesChecking Regression Assumptions with RSummary2: 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 MLMsOverview of longitudinal designs and their relationships to MLMsSummary3: Fitting 2-level Models Simple (Intercept only) Multilevel Models Interactions and Cross Level Interactions using RRandom Coefficients Models using R Centering Predictors Additional Options Parameter Estimation Method Estimation Controls Comparing Model fit Lme4 and hypothesis testing Summary4: 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 PackageSummary5: Longitudinal Data Analysis using Multilevel Models The Multilevel Longitudinal Framework Person Period Data Structure Fitting Longitudinal Models using the lme4 packageChanging the Covariance Structure of Longitudinal Models Benefits of Multilevel Modeling for Longitudinal Analysis Summary6: Graphing Data in Multilevel Contexts Plots for Linear ModelsPlotting Nested Data Using the Lattice Package Plotting Model Results using the Effects Package Summary7: Brief Introduction to Generalized Linear Models Logistic Regression Model for a Dichotomous Outcome VariableLogistic Regression Model for an Ordinal Outcome VariableMultinomial Logistic Regression Models for Count Data Poisson Regression Models for Overdispersed Count data Summary8: 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 RegressionMGLM for Count Data Random Intercept Poisson Regression Random Coefficient Poisson Regression Inclusion of additional level-2 effects to the multilevel Poisson regression model Summary9: 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 Summary10: Advanced Issues in Multilevel ModelingRobust statistics in the multilevel contextIdentifying potential outliers in single level dataIdentifying potential outliers in multilevel dataIdentifying potential multilevel outliers using RRobust and Rank Based Estimation for multilevel modelsFitting Robust and Rank Based Multilevel Models in RMultilevel LassoFitting the Multilevel Lasso in RMultivariate Multilevel ModelsMultilevel Generalized Additive ModelsFitting GAMM using RPredicting Level-2 Outcomes with Level-1 VariablesPower Analysis for Multilevel ModelsSummaryAppendix: An Introduction to R Running Statistical Analyses in R Reading Data into R Missing Data Types of Data Additional R Environment Options