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Chuenjai Sukpan; Rebecca M. Kuiper – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The (Random Intercept) Cross-Lagged Panel Model ((RI-)CLPM) is increasingly used in psychology and related fields to assess the longitudinal relationship of two or more variables on each other. Researchers are interested in the question which of the lagged effects is causally dominant receives considerable attention. However, currently used…
Descriptors: Causal Models, Psychological Studies, Multivariate Analysis, Cognitive Mapping
Julia-Kim Walther; Martin Hecht; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized…
Descriptors: Sample Size, Hierarchical Linear Modeling, Structural Equation Models, Matrices
Chi Kit Jacky Ng; Lok Yin Joyce Kwan; Wai Chan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
In the past decade, moderated mediation analysis has been extensively and increasingly employed in social and behavioral sciences. With its widespread use, it is particularly important to ensure the moderated mediation analysis will not bring spurious results. Spurious effects have been studied in both mediation and moderation analysis, but this…
Descriptors: Mediation Theory, Social Sciences, Behavioral Sciences, Predictor Variables
Bang Quan Zheng; Peter M. Bentler – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Chi-square tests based on maximum likelihood (ML) estimation of covariance structures often incorrectly over-reject the null hypothesis: [sigma] = [sigma(theta)] when the sample size is small. Reweighted least squares (RLS) avoids this problem. In some models, the vector of parameter must contain means, variances, and covariances, yet whether RLS…
Descriptors: Maximum Likelihood Statistics, Structural Equation Models, Goodness of Fit, Sample Size
French, Brian F.; Finch, W. Holmes – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Confirmatory factor analytic (CFA) procedures can be used to provide evidence of measurement invariance. However, empirical evaluation has not focused on the accuracy of common CFA steps used to detect a lack of invariance across groups. This investigation examined procedures for detection of test structure differences across groups under several…
Descriptors: Factor Analysis, Structural Equation Models, Evaluation Criteria, Error of Measurement