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 models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, ...

Citations: Anderson, James. 1988. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin. (3)411-423.

Developed by one of the world's leading authorities on the subject, Dr. Peter M. Bentler, EQS provides researchers and statisticians with a simple method for conducting the full range of structural ...

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 ...

Statistical model infrastructures at financial institutions are often developed using a piecemeal approach to model building, in which different components of complex interrelated statistical models ...

A study among 101 librarians is reported that tests individual differences important to enhanced use of the Internet. Analysis of covariance, hierarchical regression, and structural equation modeling ...

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 ...