Overview

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.

Full Product Details

Author:   Todd D. Little
Publisher:   Guilford Publications
Imprint:   Guilford Press
Edition:   2nd edition
Weight:   1.220kg
ISBN:  

9781462553143

ISBN 10:   1462553141
Pages:   616
Publication Date:   07 February 2024
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In stock  
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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

Reviews

"""In its second edition, this remains the definitive text on longitudinal SEM. The biggest strength of all the chapters is that they follow a clear organization and flow. Basic issues are presented first, followed by more advanced issues, and, finally, an example or two of the topic, with real data.""--Kristin D. Mickelson, PhD, School of Social and Behavioral Sciences, Arizona State University ""Longitudinal SEM is tricky, even for people who have experience with factor analysis and other related models. I recommend the second edition of this book to applied researchers looking for a nontechnical overview. It will help readers build their intuitive understanding of the models, which can provide a foundation for future study.""--Ed Merkle, PhD, Department of Psychological Sciences, University of Missouri–Columbia ""This is a good core textbook for an advanced course in SEM. It can even be used as a text for an introductory SEM course--as I, myself, have done with the first edition--with a bit of supplementary material. What is special about this book is the extensive use of examples, the end-of-chapter summaries (including definitions), and the detailed discussion of many problems, issues, and controversies--such as whether parceling makes sense, or how to deal with convergence issues or with longitudinal data attrition--not treated extensively in other texts.""--Douglas Baer, PhD, Department of Sociology (Emeritus), University of Victoria, British Columbia, Canada ""The equation boxes are a really nice touch that make it easier for readers to decipher the content in the equations. I am used to seeing notation detailed in paragraph-style text under an equation, but I am sold--this is a much clearer presentation style.""--Sarah Depaoli, PhD, Department of Psychological Sciences, University of California, Merced-"

Author Information

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.

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