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.
