Longitudinal Structural Equation Modeling, Second Edition (Methodology in the Social Sciences)

Todd D. Little

ISBN 10: 1462553141 / ISBN 13: 9781462553143

Published by Guilford Press 2024-02-07, 2024

About this Item

Seller Inventory # 6666-GRD-9781462553143

Bibliographic Details

• Title: Longitudinal Structural Equation Modeling, Second Edition (Methodology in the Social Sciences)
• Publisher: Guilford Press 2024-02-07
• Publication Date: 2024
• Language: English
• ISBN 10: 1462553141
• ISBN 13: 9781462553143
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

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.
 

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.