Longitudinal Structural Equation Modeling, Second Edition

图书简介

Beloved for its engaging, conversational style, this valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesn

Foreword, Noel A. Card 1. Overview and Foundations of Structural Equation Modeling - An Overview of the Conceptual Foundations of SEM - Sources of Variance in Measurement - Characteristics of Indicators and Constructs - A Simple Taxonomy of Indicators and Their Roles - Rescaling Variables - Parceling - What Changes and How? - Some Advice for SEM Programming - Philosophical Issues and How I Approach Research - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 2. Design Issues in Longitudinal Studies - Timing of Measurements and Conceptualizing Time - Modeling Developmental Processes in Context - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 3. Modern Approaches to Missing Data in Longitudinal Studies - Planning for Missing Data - Planned Missing Data Designs in Longitudinal Research - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 4. The Measurement Model - Drawing and Labeling Conventions - Defining the Parameters of a Construct - Scale Setting - Identification - Adding Means to the Model: Scale Setting and Identification with Means - Adding a Longitudinal Component to the CFA Model - Adding Phantom/Rescaling Constructs to the CFA Model - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 5. Model Fit, Sample Size, and Power - Model Fit and Types of Fit Indices - Sample Size - Power - Summary - Key Terms and Concepts Introduced in This Chapter - Recommended Readings 6. The Longitudinal CFA Model - Factorial Invariance - A Small (Nearly Perfect) Data Example - A Larger Example Followed by Tests of the Latent Construct Relations - An Application of a

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Longitudinal Structural Equation Modeling, Second Edition 纵向结构方程建模 第2版 统计学史

Longitudinal Structural Equation Modeling, Second Edition

纵向结构方程建模第2版
统计学史