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Structural Equation Modeling

SEM

Quoting from Muthén, B. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117:

“Traditionally, psychometric models have been concerned with measurement error and latent variable constructs measured with multiple indicators as in factor analysis. Structural equation modeling (SEM) took factor analysis one step further by relating the constructs to each other and to covariates in a system of linear regressions thereby purging the “structural regressions” of biasing effects of measurement error. The idea of using systems of linear regressions emanated from supply and demand modeling in econometrics and path analysis in biology. In this way, SEM consists of two ideas: latent variables and joint analysis of systems of equations. It is argued here that it is the latent variable idea that is more powerful and more generalizable. Despite its widespread use among applied researchers, SEM has still not been fully accepted in mainstream statistics. Part of this is perhaps due to poor applications claiming the establishment of causal models and part is perhaps also due to strong reliance on latent variables that are only indirectly defined. The skepticism about latent variables is unfortunate given that, as shown in this article, latent variables are widely used in statistics, although under different names and different forms.”

Following are some key references on which traditional and expanded SEM analysis in Mplus are based.

For more information, visit our General Description page.

For more resources see our Structural Equation Modeling paper topic.