Praise for the First Edition “This book will serve to greatly complement the growing number
of texts dealing with mixed models, and I highly recommend including it in one's personal …

In this chapter, we continue with Gaussian linear and additive mixed modelling methods and
discuss their application on nested data. Nested data is also referred to as hierarchical data …

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in
linear mixed-effects models can be determined using the lmer function in the lme4 package …

Multilevel statistical models allow researchers to evaluate the effects of individuals' shared
environment on an individual's outcome of interest. Finite mixture models allow the …

The dissemination of the MIXED procedure in SAS has provided a whole class of statistical
models for routine use. We believe that both the ideas be hind the techniques and their …

Multilevel analysis is a methodology for the analysis of data with complex patterns of
variability, with a focus on nested sources of such variability–pupils in classes, employees in …

Generalized linear mixed models are popular for regressing a discrete response when there
is clustering, eg in longitudinal studies or in hierarchical data structures. It is standard to …

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an
integrated treatment of data analysis for the social and behavioral sciences. It covers all of …

Presents a useful guide for applications of SEM whilst systematically demonstrating various
SEM models using Mplus Focusing on the conceptual and practical aspects of Structural …