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.
A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.
Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.
Applications in STATA®, IBM® SPSS®, SAS®, R, & HLMTM G. David Garson. addition to the editor, and the five chapters by the editor use different examples and have different content.) Garson, G. David. (2013b).
... controlling for variable X. It is analogous to the usual intraclass correlation coefficient , but now controlling for X. The formula for the ( non - residual , or raw ) intraclass correlation coefficient was just the same ...
This book provides a concise point of reference for the most commonly used regression methods.
The intervention for low - birth - weight children is described by Brooks - Gunn , Liaw , and Klebanov ( 1992 ) and Hill , Brooks - Gunn , and Waldfogel ( 2003 ) . Imbalance plots such as Figure 10.3 are commonly used ; see Hansen ...
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded ...
This is the first accessible and practical guide to using multilevel models in social research.
This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.