Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)

✍ Scribed by Todd D. Little

• Publisher: The Guilford Press
• Year: 2024
• Tongue: English
• Leaves: 643
• Edition: 2
• Category: Library

✦ Synopsis

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't work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents' emotions, and healthy aging--along with syntax and output, chapter quizzes, and the book’s figures.
 
New to This Edition:
Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux.
Chapter on longitudinal mixture modeling, with Whitney Moore.
Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne.
Chapter on Bayesian SEM, with Mauricio Garnier.
*Revised throughout with new developments and discussions, such as how to test models of experimental effects.
 

✦ Table of Contents

Cover
Front Endpaper: Greek Cheat Sheet
Half Title Page
Series Page
Title Page
Copyright
Foreword
Prologue: What’s New in the Second Edition?
Contents
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 Longitudinal SEM to a Repeated-Measures Experiment
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
7. Specifying and Interpreting a Longitudinal Panel Model
Basics of a Panel Model
The Basic Simplex Change Process
Building a Panel Model
Illustrative Examples of Panel Models
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
8. Multiple-Group Longitudinal Models
A Multiple-Group SEM
A Multiple-Group Longitudinal Model for Conducting an Intervention Evaluation
A Dynamic P-Technique Multiple-Group Longitudinal Model
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
9. The Random Intercept Cross-Lagged Panel Model
Problems with Traditional Cross-Lagged Panel Models
The Random Intercept Cross-Lagged Panel Model
Illustrative Examples of the RI-CLPM
Extensions to the RI-CLPM
Final Considerations
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
10. Mediation and Moderation
Making the Distinction between Mediators and Moderators
Moderation
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
11. Multilevel Growth Curves and Multilevel SEM
Longitudinal Growth Curve Model
Multivariate Growth Curve Models
Multilevel Longitudinal Model
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
12. Longitudinal Mixture Modeling: Finding Unknown Groups
General Background
Analysis Types
Finite Mixture Modeling Overview
Latent Class Analysis
Latent Profile Analysis
Latent Transition Analysis
Other LTA Modeling Approaches
Developments and Extensions into the Future of Finite Mixture Modeling
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
13. Bayesian Longitudinal Structural Equation Modeling
The Bayesian Perspective
Bayesian Inference
Advantages of a Bayesian Framework
MCMC Estimation
Bayesian Structural Equation Modeling
Information Criteria
Bayes Factor
Applied Example
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
14. Jambalaya: Complex Construct Representations and Decompositions
Multitrait–Multimethod Models
Pseudo-MTMM Models
Bifactor and Higher-Order Factor Models
Contrasting Different Variance Decompositions
Digestif
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
References
Author Index
Subject Index
About the Author