This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors have 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.
This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors have 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.
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
Inhaltsangabe
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.
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.
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