Two complementary schools have come to the fore in the field of Structural Equation Modelling (SEM): covariance-based SEM and component-based SEM. The first approach has been developed around Karl Jöreskog and the second one around Herman Wold under the name "PLS" (Partial Least Squares). Hwang and Takane have proposed a new component-based SEM method named Generalized Structured Component Anal...

Cost performance is the basic criteria for measuring success of any project. Since construction projects are highly dependable on resources, construction cost is significantly affected by various resource related factors. Compared to traditional methods of data analysis, Structural Equation Model (SEM) is the graphical equivalent of a mathematical representation to study relationship between de...

(Spatial) panel data are routinely modelled in discrete time (DT). However, there are compelling arguments for continuous time (CT) modelling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete representation of reality and may lead to misinterpretation of estimation results. The most compell...

Multigroup structural equation modelling (SEM) is a technique frequently used to evaluate measurement invariance in social and behavioural science research. Before version 9.2, SAS was incapable of handling multigroup SEM. However, this limitation is resolved in PROC TCALIS in SAS 9.2. For the purpose of illustration, this paper provides a step-by-step guide to programming the tests of measurem...

This paper develops a ridge procedure for structural equation modelling (SEM) with ordinal and continuous data by modelling the polychoric/polyserial/product-moment correlation matrix R. Rather than directly fitting R, the procedure fits a structural model to R(a) =R+aI by minimizing the normal distribution-based discrepancy function, where a > 0. Statistical properties of the parameter estimat...

BACKGROUND This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses. For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. Satorra and Saris developed a method for estimating the power of the likelihoo...

Structural equation modelling (SEM) is a multivariate method that incorporates ideas from regression, path-analysis and factor analysis. A Bayesian approach to SEM may enable models that reflect hypotheses based on complex theory. The development and application of Bayesian approaches to SEM has, however, been relatively slow but with modern technology and the Gibbs sampler, is now possible. Th...