Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: an intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.
Guide and Applications G. David Garson. Hierarchical Linear Modeling Guide and Applications G. David Garson Editor North Carolina State University BRIEF CONTENTS Preface xiii About the Editor xv About the.
HLM 6: Hierarchical Linear and Nonlinear Modeling
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 ...
This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models ...
Hillsdale, NJ: Lawrence Erlbaum. Cohen, J., Cohen, P., West, S., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum. DeGroot, A. D., & Spiekerman, J. A. ...
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 ...
In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multilevel data (pp. ... Sample size requirements for 2-level designs in educational research. ... Complex sample data in structural equation modeling.
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 ...
A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.