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Multilevel Modeling Using R de W. Holmes Finch

Multilevel Modeling Using R: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Autor W. Holmes Finch, Jocelyn E. Bolin, Ken Kelley
en Limba Engleză Paperback – 20 mai 2019
This book focuses on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using R as the software tool, and demonstrating the various functions available for these analyses in R.
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Specificații

ISBN-13: 9781138480674
ISBN-10: 1138480673
Pagini: 252
Ilustrații: 2 Tables, black and white
Dimensiuni: 156 x 234 x 17 mm
Greutate: 0.36 kg
Ediția:2 ed
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Cuprins

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.

Notă biografică

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®). 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®). 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®) 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.

Recenzii

"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

Descriere

This book focuses on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using R as the software tool, and demonstrating the various functions available for these analyses in R.