Structural equation models are a powerful generalization of earlier statistical approaches, such as path analysis, and a widely used tool for causal inference.

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 ...

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

Cannabis, also known as marijuana or weed, is widely consumed worldwide, whether for recreational or medicinal purposes. Over ...

The impact of residential neighborhood type on travel behavior: A structural equations modeling approach. Annals of Regional Science, (362):279–297. doi: 10.1007/s001680200083. Bamberg, S., ...

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 ...

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 ...

Building on social representations theory and applying a series of structural equation models, we tested the existence of three latent constructs of forest management and estimated whether and how ...

Information quality, encompassing relevance, clarity, accuracy, and timeliness, emerged as a foundational determinant. The ...