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

• 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.

"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

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

• 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.

"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

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

• 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.

"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