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Multilevel Analysis

Techniques and Applications

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Part of the Quantitative Methodology Series series

This book is an introduction to multilevel analysis for applied researchers featuring models for hierarchical or nested data. This book presents two types of models: The multilevel regression and multilevel covariance structures models.

Despite the book being an introduction, it includes a discussion of many extensions and special applications. As an introduction, it will be useable in courses in a variety of fields, such as psychology, education, sociology, and business. The various extensions and special applications make it useful to researchers who work in applied or theoretical research, and to methodologists that have to consult with these researchers. The basic models and examples are discussed in non-technical terms; the emphasis is on understanding the methodological and statistical issues involved in using these models. Some of the extensions and special applications contain more technical discussions, either because that is necessary for understanding what the model does, or as an introduction to more advanced treatments. Thus, the book will be useful as an introduction and as a standard reference for a large variety of applications.

Table of Contents

Contents: Preface. Introduction to Multilevel Analysis. The Basic Two-Level Regression Model: Introduction. Estimation and Hypothesis Testing in Multilevel Regression. Some Important Methodological and Statistical Issues. Analyzing Longitudinal Data. The Logistic Model for Dichotomous Data and Proportions. Cross-Classified Multilevel Models. The Multilevel Approach to Meta-Analysis. Multivariate Multilevel Regression Models. Sample Sizes and Power Analysis in Multilevel Regression. Advanced Methods for Estimation and Testing. Multilevel Factor Models. Multilevel Path Models. Latent Curve Models. Appendix: Data and Stories.

Reviews

"The author has chosen a technical level for the presentation that allows precision in the model specifications and the discussion of more advanced issues without detracting from its applied focus. This book provides a good reference for those who want to grasp the essentials of this widely used methodology."

—Short Book Reviews

"…provides an intuitive understanding of the topic while building on a basic foundation of classical multiple regression….is very well organized and is a nice resource to supplement other texts….I especially recommend this book to practitioners and consultants who work in social-science-related disciplines, although it is also handy for anyone that works with mixed models."

—Journal of the American Statistical Association