Multilevel Modeling Using R
2nd Edition
Published on 20. May 2019
Book
Paperback/Softback
242 pages
978-1-138-48067-4 (ISBN)
Description
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.
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.
Reviews / Votes
"This book is the second edition of a hugely popular title on multilevel modelling (MLM) using R software. Assuming a basic understanding of how a linear regression model works, if someone is looking for a complete reference on how to fit multilevel models with R, then look no further. Even for those not accustomed to the mathematical details of regression modelling, the provided overview with practical examples and R code should get one up to speed. This book is concise, to the point, and a hands-on, how-to reference on multilevel modelling. Through their clear writing style, the authors have provided answers to all of the essential questions a practitioner might have in fitting a multilevel model. In essence, the book presents straightforward explanations of basic MLM, multilevel generalized linear models, Bayesian multilevel modelling, multivariate multilevel modelling, and how to fit them using R."- Enayet Raheem, ISCB News, July 2020
More details
Series
Edition
2nd edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Paperback (trade)
Unsewn / adhesive bound
Illustrations
2 s/w Tabellen
2 Tables, black and white
Dimensions
Height: 231 mm
Width: 156 mm
Thickness: 17 mm
Weight
381 gr
ISBN-13
978-1-138-48067-4 (9781138480674)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
W. Holmes Finch | Jocelyn E. Bolin
Multilevel Modeling Using R
Book
04/2024
3rd Edition
Chapman & Hall/CRC
€84.20
Shipment within 10-20 days
Additional editions
W. Holmes Finch | Jocelyn E. Bolin | Ken Kelley
Multilevel Modeling Using R
Book
05/2019
2nd Edition
CRC Press
€154.50
Article exhausted; check for reprint
Persons
W. Holmes Finch is a professor in the Department of Educational Psychology at Ball State University, where he teaches courses on factor analysis, structural equation modeling, categorical data analysis, regression, multivariate statistics, and measurement to graduate students in psychology and education. Dr. Finch is also an Accredited Professional Statistician (PStat (R)). He earned a PhD from the University of South Carolina. His research interests include multilevel models, latent variable modeling, methods of prediction and classification, and nonparametric multivariate statistics.
Jocelyn E. Bolin is an assistant professor in the Department of Educational Psychology at Ball State University, where she teaches courses on introductory and intermediate statistics, multiple regression analysis, and multilevel modeling to graduate students in social science disciplines. Dr. Bolin is a member of the American Psychological Association, the American Educational Research Association, and the American Statistical Association and is an Accredited Professional Statistician (PStat (R)). She earned a PhD in educational psychology from Indiana University Bloomington. Her research interests include statistical methods for classification and clustering and use of multilevel modeling in the social sciences.
Ken Kelley is the Viola D. Hank Associate Professor of Management in the Mendoza College of Business at the University of Notre Dame. Dr. Kelley is also an Accredited Professional Statistician (PStat (R)) and associate editor of Psychological Methods. His research involves the development, improvement, and evaluation of quantitative methods, especially as they relate to statistical and measurement issues in applied research. He is the developer of the MBESS package for the R statistical language and environment.
Jocelyn E. Bolin is an assistant professor in the Department of Educational Psychology at Ball State University, where she teaches courses on introductory and intermediate statistics, multiple regression analysis, and multilevel modeling to graduate students in social science disciplines. Dr. Bolin is a member of the American Psychological Association, the American Educational Research Association, and the American Statistical Association and is an Accredited Professional Statistician (PStat (R)). She earned a PhD in educational psychology from Indiana University Bloomington. Her research interests include statistical methods for classification and clustering and use of multilevel modeling in the social sciences.
Ken Kelley is the Viola D. Hank Associate Professor of Management in the Mendoza College of Business at the University of Notre Dame. Dr. Kelley is also an Accredited Professional Statistician (PStat (R)) and associate editor of Psychological Methods. His research involves the development, improvement, and evaluation of quantitative methods, especially as they relate to statistical and measurement issues in applied research. He is the developer of the MBESS package for the R statistical language and environment.
Content
1. Basic Linear Models. 2. Two- and Three-Level MLMs for Continuous Outcomes, MLM Applications for Longitudinal Designs and Dyadic Designs. 3. MLMs for Dichotomous Logistic Regression and Separately Ordinal Logistic Regression.4. MLMs for Other Generalized Linear Models. 5. Multivariate Multilevel Methods. 6. Nonparametric and Robust Approaches.