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 ...

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

This course is an introduction to structural equation modeling (SEM). SEM represents a general approach to the statistical examination of the fit of a theoretical model to empirical data. Topics ...

The basics of variation - means and variances are considered, followed by description of i) the tracing rules of path analysis and ii) matrix representation of path models. The discussion is ...

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 ...

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 ...