Longitudinal Structural Equation Modeling

by Todd D. Little PhD, Noel A. Card PhD (Foreword by)

Overview

This book has been replaced by Longitudinal Structural Equation Modeling, Second Edition, ISBN 978-1-4625-5314-3.
 

Product Details

ISBN-13:	9781462553143
Publisher:	Guilford Publications, Inc.
Publication date:	01/02/2024
Series:	Methodology in the Social Sciences Series
Edition description:	Second Edition
Pages:	616
Product dimensions:	7.00(w) x 10.10(h) x 1.30(d)

About the Author

Todd D. Little, PhD, is Professor of Educational Psychology, Leadership, and Counseling at Texas Tech University, in the Research, Evaluation, Measurement, and Statistics program. He is also an Extraordinary Professor at the Optentia Research Focus Area, North-West University, Vanderbijlpark, South Africa. Dr. Little is a Fellow of the American Association for the Advancement of Science; the American Psychological Association (APA) Divisions 5, 7, and 15; and the Association for Psychological Science. He is editor of Guilford’s Methodology in the Social Sciences series and past president of APA Division 5 (Evaluation, Measurement, and Statistics). Dr. Little organizes and teaches in his renowned “Stats Camp” (statscamp.org) each June. Partly because of the impact and importance of Stats Camp, Dr. Little was awarded the Cohen Award for Distinguished Contributions to Teaching and Mentoring from APA Division 5 and the inaugural Teaching and Mentoring Award from the Society for Research in Child Development.

Table of Contents

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 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, Danny Osborne and Todd D. Little
- 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, E. Whitney G. Moore and Todd D. Little
- 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, Mauricio Garnier-Villarreal and Todd D. Little
- 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

Interviews

Researchers and graduate students in psychology, education, management, family studies, public health, sociology, and social work. May serve as a text in graduate-level courses on longitudinal models, latent variable models, growth curve models, advanced SEM, or advanced quantitative methods.

Editorial Reviews

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From the Publisher