Handbook of Advanced Multilevel Analysis

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.

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.

Joop J. Hox is Professor and Chair of Social Science Methodology at Utrecht University. A Fellow of the Royal Statistical Society and a founding member of the European Association of Methodology, his recent publications focus on survey non-response, interviewer effects, survey data quality, missing data, and multilevel analysis of regression and structural equation models. He is the author of Multilevel Analysis, Second Edition (Routledge) and a co-editor of the International Handbook of Survey Methodology (Routledge). J. Kyle Roberts is an Associate Professor in the Annette Caldwell Simmons School of Education and Human Development at Southern Methodist University. Dr. Roberts has conducted numerous training sessions on multilevel analysis at annual meetings of the American Psychological Association, the American Educational Research Association, and the Southwest Educational Research Association. He has authored several book chapters and articles on multilevel analysis, and currently works with school districts in the development of value-added models for student and teacher