Structural Equation Model
Introduction
Structural Equation Modeling: A Multidisciplinary Journal, 31(1)
INTEREST CATEGORY: MARKETING RESEARCH
POSTING TYPE: TOCs
An Evaluation of Non-Iterative Estimators in Confirmatory Factor Analysis |
—Sara Dhaene & Yves Rosseel [] []
Bayesian Inference of Dynamic Mediation Models for Longitudinal Data
—Saijun Zhao, Zhiyong Zhang & Hong Zhang [] []
Dynamic Fit Index Cutoffs for Hierarchical and Second-Order Factor Models
—Daniel McNeish & Patrick D. Manapat [] []
Leveraging Observation Timing Variability to Understand Intervention Effects in Panel Studies: An Empirical Illustration and Simulation Study |
—Andrea Hasl, Manuel Voelkle, Charles Driver, Julia Kretschmann & Martin Brunner [] []
The Impact of Ignoring Cross-loadings on the Sensitivity of Fit Measures in Measurement Invariance Testing
—Chunhua Cao & Xinya Liang [] []
Revisiting Savalei’s (2011) Research on Remediating Zero-Frequency Cells in Estimating Polychoric Correlations: A Data Distribution Perspective
—Tong-Rong Yang & Li-Jen Weng [] []
The SEM Reliability Paradox in a Bayesian Framework
—Timothy R. Konold & Elizabeth A. Sanders [] []
Temporal Misalignment in Intensive Longitudinal Data: Consequences and Solutions Based on Dynamic Structural Equation Models
—Xiaohui Luo & Yueqin Hu [] []
The Impact of Omitting Confounders in Parallel Process Latent Growth Curve Mediation Models: Three Sensitivity Analysis Approaches
—Xiao Liu, Zhiyong Zhang, Kristin Valentino & Lijuan Wang [] []
Studying Between-Subject Differences in Trends and Dynamics: Introducing the Random Coefficients Continuous-Time Latent Curve Model with Structured Residuals
—Julian F. Lohmann, Steffen Zitzmann & Martin Hecht [] []
Teacher’s Corner
Benefits of Doing Generalizability Theory Analyses within Structural Equation Modeling Frameworks: Illustrations Using the Rosenberg Self-Esteem Scale
—Walter P. Vispoel, Hyeri Hong & Hyeryung Lee [] []
Latent Growth Models for Count Outcomes: Specification, Evaluation, and Interpretation
—Daniel Seddig [] []
Book Review
Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences) By Ross Jacobucci, Kevin J. Grimm, Zhiyong Zhang. New York, NY: The Guilford Press, (2023), 416 pp. $93.00 (Hardback), ISBN: 9781462552931. $62.00 (Paperback), ISBN: 9781462552924. $62.00 (PDF).
—Aszani Aszani & Ruslan Anwar []