R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the ...

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. This ...

Structural equation modeling techniques and associated computer programs are now widely available for use in the social and behavioral sciences. However, most reported applications are limited 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 ...

Journal of Marketing Theory and Practice, Vol. 16, No. 4, Structural Equation Modeling (Fall, 2008), pp. 287-298 (12 pages) The authors review issues related to the application of structural equation ...

Department of Mathematics, Science and Technology Education, Faculty of Education, University of Zululand, KwaDlangezwa, South Africa Introduction: Mathematics achievement is the level of knowledge, ...

The advent of electronic storage of medical records and the internet has led to an increase in the use of online medical records, thereby enhancing doctor–patient communication and facilitating ...

This study examines the application of information criteria in the selection of multilevel structural equation models (MSEM), analyzing different estimation methods (ULS, GLS, and ML) and statistical ...

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 ...