Main description:

This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.

Contents:

to Multilevel Analysis.- Bayesian Multilevel Analysis and MCMC.- Diagnostic Checks for Multilevel Models.- Optimal Designs for Multilevel Studies.- Many Small Groups.- Multilevel Models for Ordinal and Nominal Variables.- Multilevel and Related Models for Longitudinal Data.- Non-Hierarchical Multilevel Models.- Multilevel Generalized Linear Models.- Missing Data.- Resampling Multilevel Models.- Multilevel Structural Equation Modeling.