This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion.
Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis.
Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.
"An excellent volume on an array of next-steps for learning in the multilevel arena. The result is a superb resource on the state-of-the-art and the cutting edge of multilevel models. Each chapter brings the reader up to speed on an issue by providing a valuable synthesis of the existing analytic and empirical statistical work often enhanced by sound thinking and guidance. … Aside from providing a valuable reference for researchers and instructors, it provides a deep well of possibilities for future statistical work in this area." - Jason T. Newsom, Portland State University, USA, in The American Statistician
"Perhaps the most interesting aspect of this handbook is its focus on a range of non-standard multilevel analysis topics such as structural equation modelling (SEM), item response theory (IRT), mixture models and dyadic data analysis. It is the timely inclusion of these topics … which distinguishes this handbook from competing books. … Overall I enjoyed this handbook and would recommend it to advanced applied researchers with a firm grounding in multilevel analysis." - George Leckie, University of Bristol, UK in Journal of the Royal Statistical Society
"This is a wonderful addition to the field of multilevel modeling. It is a state-of-the-art contribution from the frontiers of the field. Chapters are written by leading authorities and cover a wide array of models from introductory to more advanced. This book will become an essential reference resource." - George A. Marcoulides, University of California, Riverside, USA
"The Handbook … covers a wide range of topics, both technical and applied; and the chapters address some of the most crucial and controversial issues in the field of multilevel modeling. This book is sure to become a classic reference, and I plan to keep it within an arms’ length of my computer at all times!" - Betsy McCoach, University of Connecticut, USA
"This book presents a wide range of well-selected topics, like multilevel latent variable models, longitudinal data analysis, multilevel models for ordinal outcomes, design, model fit, bootstrapping, and missing data. Especially useful are the examples and the accompanying software codes." - Rolf Steyer, University of Jena, Germany
"An outstanding set of authors who should advance the field’s understanding about … multilevel modeling…the coverage is excellent… .I would… recommend it to students who are doing dissertations on multilevel analysis… [and] in programs that are training methodologists. …An excellent resource." - Ron Heck, University of Hawaii – Manoa, USA
"It makes an important contribution to the field by bringing together many top experts to produce a ‘one-stop’ source for cutting-edge advanced MLM procedures. I would purchase this book and … recommend it … for students with strong quantitative interests using MLM… psychologists, child developmental, educational, and sociological researchers, to name just a few, would find relevance in this work." - Noel A. Card, University of Arizona, USA
"A useful contribution to a rapidly developing area [that] promises to further fuel growth and interest in this area. …The breadth of coverage and … depth of treatment … is a real strength…. [it] fills a gap in the material currently available." - Scott L. Thomas, Claremont Graduate University, USA
Part 1. Introduction. J. Hox, J.K. Roberts, Multilevel Analysis: Where We Were and Where We Are. Part 2. Multilevel Latent Variable Modeling (LVM). B. Muthén, T. Asparouhov, Beyond Multilevel Regression Modeling: Multilevel Analysis in a General Latent Variable Framework. A. Kamata, B. Vaughn, Multilevel IRT Modeling. J. Vermunt, Mixture Models for Multilevel Data Sets. Part 3. Multilevel Models for Longitudinal Data. J. Hox, Panel Modeling: Random Coefficients and Covariance Structures. R.D. Stoel, F.G. Garre, Growth Curve Analysis using Multilevel Regression and Structural Equation Modeling. Part 4. Special Estimation Problems. D. Hedeker, R.J. Mermelstein, Multilevel Analysis of Ordinal Outcomes Related to Survival Data. E.L. Hamaker, I. Klugkist, Bayesian Estimation of Multilevel Models. H. Goldstein, Bootstrapping in Multilevel Models. S. van Buuren, Multiple Imputation of Multilevel Data. J. Kim, C.M. Swoboda, Handling Omitted Variable Bias in Multilevel Models: Model Specification Tests and Robust Estimation. J.K. Roberts, J.P. Monaco, H. Stovall, V. Foster, Explained Variance in Multilevel Models. E.L. Hamaker, P. van Hattum, R.M. Kuiper, H. Hoijtink, Model Selection Based on Information Criteria in Multilevel Modeling. M. Moerbeek, S. Teerenstra, Optimal Design in Multilevel Experiments. Part 5. Specific Statistical Issues. J. Algina, H. Swaminathan, Centering in Two-Level Nested Designs. S.N. Beretvas, Cross-Classified and Multiple Membership Models. D.A. Kenny, D.A. Kashy, Dyadic Data Analysis using Multilevel Modeling.