Structural equation modeling is a powerful tool for dealing with these difficulties using a simultaneous equation framework with unobserved constructs and manifest indicators which are error-prone.

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

Structural Equation Modeling (SEM) Structural Equation Modeling (SEM) Course Topics Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory ...

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.

In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step ...

The authors review issues related to the application of structural equation modeling (SEM) in marketing. The discussion begins by considering issues related to the process of applying SEM in empirical ...

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 ...

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 ...