Bayesian methods in Structural Equation Modeling (SEM) represent a paradigm shift in statistical analysis, integrating prior beliefs with empirical data to derive robust parameter estimates.

Christian Damgaard, Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework, Ecosystems, Vol. 22, No. 1 (January 2019), pp. 152-164 ...

Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance ...

This paper takes an objective look at how papers using structural equation models are received in the review process of academic marketing research journals. The focus is examining whether or not ...

We propose an empirical framework, spurred by recent developments in the implementation of generalized structural equation modeling (GSEM), which brings to bear a modular and all-inclusive approach to ...

Advanced Topics in R: parallel processing, structural equation modeling, and the bootstrap Advanced Topics in R: parallel processing, structural equation modeling, and the bootstrap Course Topics R is ...

Regulators need a method that is versatile, is easy to use and can handle complex path models with latent (not directly observable) variables. In a first application of partial least squares ...

A new research paper was published in Volume 12 of Oncoscience on July 31, 2025, titled “Burnout among oncology nurses and technicians in Morocco: Prevalence, risk factors, and structural equation ...