Handbook of advanced multilevel analysis
著者
書誌事項
Handbook of advanced multilevel analysis
（European Association of Methodology series）
Routledge, c2011
- : hbk
大学図書館所蔵 件 / 全14件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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.
目次
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. Muthen, 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.
「Nielsen BookData」 より
