Handbook of Multilevel Analysis

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 bio-medical sciences. The models used for this type of data are linear and nonlinear regression models that account for observed and unobserved heterogeneity at the various levels in the data.

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. The authors of the chapters are the leading experts in the field.

Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is useful for empirical researchers in these fields. Prior knowledge of multilevel analysis is not required, but a basic knowledge of regression analysis, (asymptotic) statistics, and matrix algebra is assumed.

Discusses complex data structures with a hierarchical structure, e.g., pupils nested within schools Distinguished collection of contributors Includes supplementary material: sn.pub/extras

Inhalt
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.