Commentary: Is structural equation modelling a step forward for epidemiologists?

One step forward

The Next Step Forward Is to Take a Step Back.

Understanding the causes of diabetes is at the heart of most diabetes research. The field of causal inference has made dramatic advances in methodology that allows for causal inference from observational and not experimental data (1). One such methodological advancement is the use of genetic markers as instrumental variables, or so-called Mendelian randomization (MR) (2). MR relies on the laws ...

Structural Equation Modelling for small samples

Covariance-based SEM is usually used with an objective of model validation and needs a large sample (what is large varies from an author to another: more than 100 subjects and preferably more than 200 subjects are often mentioned). Component-based SEM is mainly used for score computation and can be carried out on very small samples. A research based on 6 subjects has been published by Tenenhaus...

Promoting Entrepreneurial Behavior among Agricultural Students: A Two-Step Approach to Structural Equation Modeling

Increasing rate of unemployment among the graduates of Iranian higher agricultural education system has been developed into one of the major socio-economic problems of the nation. Entrepreneurship is one of the most important factors contributing to economic and social development, that is, it is a main driver for employment creation. As such, many policy initiatives in Iran attempt to pull agr...

Component-based Structural Equation Modelling

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...