Why use a structural equation model?

Dan talks about three principal advantages of structural equation models (SEMs) relative to more traditional analytic techniques, like the linear regression model. These include...

(1) The ability to represent constructs as latent variables that are uncontaminated by measurement error
(2) Falsification tests and indices of fit with to evaluate the tenability of a proposed theoretical model
(3) Flexibility to allow researchers to specify statistical models that more closely match theory

Dan describes these advantages using a specific example on the factors that relate to young children's popularity with peers.

In addition to these three principal advantages of the SEM, there are many other ways that the model can be expanded and used to address interesting theoretical questions. For instance, a variety of SEMs exist for analyzing longitudinal data, including latent growth curve models and latent change score models. SEMs also provide a powerful framework within which to evaluate population heterogeneity, including differences over known groups (e.g., boys and girls) or latent groups (e.g., clusters of individuals for whom predictive relationships differ). For those interested in learning more, we offer summer training seminars on SEM and longitudinal SEM, see http://www.curranbauer.org/training/.