# Multilevel modeling using R

##### By: Finch, W. Holmes

##### Contributor(s): Bolin, Jocelyn E [Co-author] | Kelly, Ken [Co-author]

Material type: TextSeries: Chapman & Hall/CRC: statistics in the social and behavioral sciencesPublisher: New York CRC Press 2019Edition: 2ndDescription: ix, 242 p.: ill. Includes bibliographical references and indexISBN: 9780038480674Subject(s): Social sciences - Statistical methods | Multivariate analysis | R (Computer program language)DDC classification: 005.55 Summary: 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. https://www.routledge.com/Multilevel-Modeling-Using-R/Finch-Bolin-Kelley/p/book/9781138480674Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library General Stacks | Slot 82 (0 Floor, West Wing) | Non-fiction | 005.55 F4M8 (Browse shelf) | Available | 203190 |

Table of content

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 Packag

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

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.

https://www.routledge.com/Multilevel-Modeling-Using-R/Finch-Bolin-Kelley/p/book/9781138480674

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