Handbook of Advanced Multilevel Analysis (European Association of Methodology Series)

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. Title......Page 4 Copyright......Page 5 Contents......Page 6 Preface......Page 8 Section I: Introduction......Page 10 1 Multilevel Analysis: Where We Were and Where We Are......Page 12 Section II: Multilevel Latent Variable Modeling (LVM)......Page 22 2 Beyond Multilevel Regression Modeling: Multilevel Analysis in a General Latent Variable Framework......Page 24 3 Multilevel IRT Modeling......Page 50 4 Mixture Models for Multilevel Data Sets......Page 68 Section III: Multilevel Models for Longitudinal Data......Page 92 5 Panel Modeling: Random Coefficients and Covariance Structures......Page 94 6 Growth Curve Analysis Using Multilevel Regression and Structural Equation Modeling......Page 106 Section IV: Special estimation Problems......Page 122 7 Multilevel Analysis of Ordinal Outcomes Related to Survival Data......Page 124 8 Bayesian Estimation of Multilevel Models......Page 146 9 Bootstrapping in Multilevel Models......Page 172 10 Multiple Imputation of Multilevel Data......Page 182 11 Handling Omitted Variable Bias in Multilevel Models: Model Specification Tests and Robust Estimation......Page 206 12 Explained Variance in Multilevel Models......Page 228 13 Model Selection Based on Information Criteria in Multilevel Modeling......Page 240 14 Optimal Design in Multilevel Experiments......Page 266 Section V: Specific Statistical Issues......Page 292 15 Centering in Two-Level Nested Designs......Page 294 16 Cross-Classified and Multiple-Membership Models......Page 322 17 Dyadic Data Analysis Using Multilevel Modeling......Page 344 Author Index......Page 380 Subject Index......Page 388 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