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Mauricio Garnier-Villarreal; Terrence D. Jorgensen – Grantee Submission, 2024
Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are use to modify the original model. In the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are used to select the parameters that can be added to improve the fit. The…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Indexes
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Ke-Hai Yuan; Zhiyong Zhang – Grantee Submission, 2022
Structural equation modeling (SEM) is a widely used technique for studies involving latent constructs. While covariance-based SEM (CB-SEM) permits estimating the regression relationship among latent constructs, the parameters governing this relationship do not apply to that among the scored values of the constructs, which are needed for…
Descriptors: Psychometrics, Structural Equation Models, Scores, Least Squares Statistics
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Ke-Hai Yuan; Yongfei Fang – Grantee Submission, 2023
Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and…
Descriptors: Structural Equation Models, Regression (Statistics), Weighted Scores, Comparative Analysis
Yamashita, Takashi; Smith, Thomas J.; Cummins, Phyllis A. – Grantee Submission, 2020
Background: Several statistical applications including Mplus, STATA, and R are available to conduct analyses such as structural equation modeling and multi-level modeling using large-scale assessment data that employ complex sampling and assessment designs and that provide associated information such as sampling weights, replicate weights, and…
Descriptors: Learning Analytics, Computer Software, Syntax, Adults
Cain, Meghan K.; Zhang, Zhiyong – Grantee Submission, 2018
Despite its importance to structural equation modeling, model evaluation remains underdeveloped in the Bayesian SEM framework. Posterior predictive p-values (PPP) and deviance information criteria (DIC) are now available in popular software for Bayesian model evaluation, but they remain under-utilized. This is largely due to the lack of…
Descriptors: Bayesian Statistics, Structural Equation Models, Monte Carlo Methods, Sample Size
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Lee, Taehun; Cai, Li; Kuhfeld, Megan – Grantee Submission, 2016
Posterior Predictive Model Checking (PPMC) is a Bayesian model checking method that compares the observed data to (plausible) future observations from the posterior predictive distribution. We propose an alternative to PPMC in the context of structural equation modeling, which we term the Poor Persons PPMC (PP-PPMC), for the situation wherein one…
Descriptors: Structural Equation Models, Bayesian Statistics, Prediction, Monte Carlo Methods
Stormshak, Elizabeth A.; DeGarmo, David; Garbacz, S. Andrew; McIntyre, Laura Lee; Caruthers, Allison – Grantee Submission, 2020
In this study we examined the efficacy of a version of the Family Check-Up (FCU) adapted for kindergarten school entry with regard to parenting skills during the transition to school. We also examined whether improvements in parenting skills would mediate improvements in parent- and teacher-rated child behavior problems from kindergarten to second…
Descriptors: Parenting Skills, Kindergarten, Parent Attitudes, Teacher Attitudes
Rodas, Naomi V.; Eisenhower, Abbey; Blacher, Jan – Grantee Submission, 2017
Children with autism spectrum disorder (ASD) are at heightened risk for developing comorbid psychological disorders, including anxiety disorders, which may be further exacerbated by the presence of externalizing behaviors. Here, we examined how structural language and pragmatic language predicted anxiety and externalizing behaviors. Participants…
Descriptors: Pragmatics, Autism, Pervasive Developmental Disorders, Language Skills