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Njål Foldnes; Jonas Moss; Steffen Grønneberg – Structural Equation Modeling: A Multidisciplinary Journal, 2025
We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable and biased in known directions. To take this into account, we design model-based trend predictions to approximate the…
Descriptors: Goodness of Fit, Structural Equation Models, Robustness (Statistics), Prediction
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Jie Fang; Zhonglin Wen; Kit-Tai Hau – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e.,…
Descriptors: Structural Equation Models, Mediation Theory, Data Analysis, Longitudinal Studies
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Gyeongcheol Cho; Heungsun Hwang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed…
Descriptors: Prediction, Methods, Networks, Simulation
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Kjorte Harra; David Kaplan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The present work focuses on the performance of two types of shrinkage priors--the horseshoe prior and the recently developed regularized horseshoe prior--in the context of inducing sparsity in path analysis and growth curve models. Prior research has shown that these horseshoe priors induce sparsity by at least as much as the "gold…
Descriptors: Structural Equation Models, Bayesian Statistics, Regression (Statistics), Statistical Inference
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Lanza, Stephanie T.; Tan, Xianming; Bray, Bethany C. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with…
Descriptors: Structural Equation Models, Monte Carlo Methods, Comparative Analysis, Statistical Analysis
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Marsh, Herbert W.; Ludtke, Oliver; Trautwein, Ulrich; Morin, Alexandre J. S. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing…
Descriptors: Structural Equation Models, Goodness of Fit, Profiles, Prediction
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Lippke, Sonia; Nigg, Claudio R.; Maddock, Jay E. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
This is the first study to test whether the stages of change of the transtheoretical model are qualitatively different through exploring discontinuity patterns in theory of planned behavior (TPB) variables using latent multigroup structural equation modeling (MSEM) with AMOS. Discontinuity patterns in terms of latent means and prediction patterns…
Descriptors: Physical Activities, Structural Equation Models, Physical Activity Level, Prediction