"This book presents the theory and practice of major multilevel modeling techniques in a variety of contexts using R as the software tool. It describes the applications and extensions of multilevel modeling with special emphasis on the use of R to conduct the analyses and interpret the resulting output. The book is designed for researchers, data analysts, and upper-level undergraduate and graduate students taking a course on multilevel modeling or statistical modeling."--
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.
... 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 ...
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).
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 book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component.
This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
This book provides a broad overview of basic multilevel modeling issues and illustrates techniques building analyses around several organizational data sets.
The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment.
This book provides a concise point of reference for the most commonly used regression methods.