Small Sample Corrections to Model Fit Criteria for Latent Change Score Models.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
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Includes bibliographical references and index.
CONTENTS NOTE
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Cover; Half Title; Series Information; Title Page; Copyright Page; Table of contents; Foreword; Contributors; Introduction and Section Overview; Section I: Extensions of Latent Change Score Models; Section II: Measurement and Testing Issues in Longitudinal Modeling; Section III: Novel Applications of Multivariate Longitudinal Methodology; Section I Extensions of Latent Change Score Models; 1 Methodological Issues and Extensions to the Latent Difference Score Framework; The Bivariate Dual Change Score (BDCS) Model; IC Specifications in the BDCS; Stochastic BDCS Model; Empirical Illustration.
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Developmental Changes in Fluid ReasoningDiscussion; Summary of Findings; Methodological and Substantive Implications; Notes; References; Appendix; Sample Code for Analyses; 3 Individually Varying Time Metrics in Latent Change Score Models; Latent Change Score Models; Estimation of Latent Change Score Models; Structural Equation Modeling Framework; Nonlinear Multilevel Modeling Framework; JAGS; Illustrative Example; Data; SAS Programming; NLMIXED Output; JAGS; Structural Equation Modeling Software; Discussion; Note; References; 4 Latent Change Score Models with Curvilinear Constant Bases.
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DiscussionLimitations; Closing Remarks; Notes; References; Appendix; Sample dynr Code for Fitting the Stochastic BDCS Models in the Illustrative Example.; 2 Discrete- and Semi-continuous Time Latent Change Score Models of Fluid Reasoning Development from Childhood to Adolescence; Development of Fluid Reasoning; Models to Examine Changes in Fluid Reasoning across Measurement Occasions; Models to Examine Dynamics of Fluid Reasoning and Underlying Developmental Mechanisms; Method; Participants and Measures; Data Description; Results; Change in Fluid Reasoning Across Measurement Occasions.
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IntroductionMethods; Latent Constant Change Score Model; Latent Quadratic Constant Change Score Model; A Latent Change Score Model Representing Latent Basis Curve Model; Bivariate Latent Quadratic Curve Model Based on Latent Change Score Approach; Bivariate Quadratic Constant Latent Change Score Model with Coupling Effect; Bivariate Quadratic Constant Latent Change Score Model with Self-Feedback and Coupling Effect; Discussion; References; 5 Regularized Estimation of Multivariate Latent Change Score Models; Regularized Estimation of Multivariate Latent Change Score Models.
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Latent Change Score FrameworkExploratory Model Specification and Testing; Rationale; Regularization; Purpose; Univariate LCS Regularization; Time-Varying Effects Regularization; Bivariate LCS Regularization; Discussion; Notes; References; 6 The Reticular Action Model; Setting the Stage; The Breakthrough: RAM Algebra and RAM Path Analysis; Some Personal History; How Our Thinking About Modeling Has Changed; The Future of RAM; Notes; References; Section II Measurement and Testing Issues in Longitudinal Modeling; 7 Small Sample Corrections to Model Fit Criteria for Latent Change Score Models.
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SUMMARY OR ABSTRACT
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This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes. The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.