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Multilevel Modeling

Methodological Advances, Issues, and Applications

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Series: Multivariate Applications Series.

This book illustrates the current work of leading multilevel modeling (MLM) researchers from around the world.

The book's goal is to critically examine the real problems that occur when trying to use MLMs in applied research, such as power, experimental design, and model violations. This presentation of cutting-edge work and statistical innovations in multilevel modeling includes topics such as growth modeling, repeated measures analysis, nonlinear modeling, outlier detection, and meta analysis.

This volume will be beneficial for researchers with advanced statistical training and extensive experience in applying multilevel models, especially in the areas of education; clinical intervention; social, developmental and health psychology, and other behavioral sciences; or as a supplement for an introductory graduate-level course.

Table of Contents

Contents: Preface. R. Cudeck, S.H.C. du Toit, Nonlinear Multilevel Models for Repeated Measures Data. M. Seltzer, K. Choi, Sensitivity Analysis for Hierarchical Models: Downweighting and Identifying Extreme Cases Using the t Distribution. P.M. Bentler, J. Liang, Two-Level Mean and Covariance Structures: Maximum Likelihood via an EM Algorithm. B. Muthén, S-T. Khoo, D.J. Francis, C.K. Boscardin, Analysis of Reading Skills Development From Kindergarten Through First Grade: An Application of Growth Mixture Modeling to Sequential Processes. J.J. Hox, E.D. de Leeuw, Multilevel Models for Meta-Analysis. B. Jo, B.O. Muthén, Longitudinal Studies With Intervention and Noncompliance: Estimation of Causal Effects in Growth Mixture Modeling. E.R. Baumler, R.B. Harrist, S. Carvajal, Analysis of Repeated Measures Data. N. Bachmann, R. Hornung, The Development of Social Resources in a University Setting: A Multilevel Analysis. A. Fielding, Ordered Category Responses and Random Effects in Multilevel and Other Complex Structures. D. Hutchison, Bootstrapping the Effect of Measurement Errors on Apparent Aggregated Group-Level Effects. R. Ecob, G. Der, An Iterative Method for the Detection of Outliers in Longitudinal Growth Data Using Multilevel Models. K.J. Rowe, Estimating Interdependent Effects Among Multilevel Composite Variables in Psychosocial Research: An Example of the Application of Multilevel Structural Equation Modeling. S.P. Reise, N. Duan, Design Issues in Multilevel Studies.

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