**1. Introduction**

SEM is a multivariate method whose use has grown exponentially in medicine and health sciences. The SEM is a statistical method considered as a causal model that includes, among other techniques, the Linear Regression Model (LRM), Factor Analysis (FA), Confirmatory Factor Analysis (CFA), and Path Analysis. This statistical model can help the researcher to test or confirm theoretical models or hypotheses and validate causal relations between variables, which can be latent and observed, or only between observed variables.

When a researcher is interested in investigating the causal relationships between a grouping of variables that define a factor or latent variable, he is interested in proving or confirming (or discontinuing) that his hypothetical model is appropriate for the data analyzed.

As a result, the researcher has the following options: a) when the hypothetical model is confirmed by the analyzed data, he can include new elements to the original model and then analyze that new structure; b) when the hypothetical model is not appropriate for the analyzed data, the original model can be modified or a new model can be tested.

Pearl cited by Kline [1] defines SEM as a causal method that considers as input (a) a set of qualitative causal hypotheses based on theory or the results of empirical studies represented by a set of equations, (b) a set of questions about the causal relationship between factors or latent variables of interest. Many SEM applications focus on non-experimental or observational designs and data from quasi-experimental or experimental designs.
