Multilevel Modeling Using R

Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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.

　

252 pages, Kindle Edition

First published December 26, 2013

Reviews

[Terran M · Rating 2 out of 5]

This brief book is designed in the model of a practitioner's guide with just enough theory to understand how to call and interpret the R functions. Unfortunately, it partially fails in this; the mathematical background it provides is too thin to explain several key concepts and they are glossed over.

For example, when explaining why to use multilevel models, the book compares them to a strawman of not including the groupings at all; a more meaningful comparison would have been to a linear model which included the grouping variable as a factor. A better explanation is provided for free in one paragraph in section 2.2 of the lmer vignette documentation.

As another example, in describing the lme4 syntax, the book explains how to specify that random slopes are correlated or uncorrelated, but does not explain what that actually means, what it translates to in equations, or how it actually impacts the model fit. The term "shrinkage" is never mentioned.

I would recommend the multilevel modeling book by Gelman and Hill instead, or the unfinished online PDF by Doug Bates.