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

The assessment of change in patient-reported outcomes is hindered by the fact that there are different types of change. Besides 'true' change, different types of response shift, such as recalibration, ...

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated ...

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 ...

introduction to basic concepts, principles, and applications of structural equation modeling including path analysis, confirmatory latent variable models, multiple-group modeling, and latent growth ...

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 ...