**Abstract**

Structural Equation Models (SEM) are very useful and, with a wide range of practical applications in many fields of science, in medicine and health sciences, have increased interest in their usefulness. This chapter is divided into three sections. The first includes concepts, notation, and theoretical aspects of SEM, such as path diagrams, measurement model, confirmatory factor analysis, structural regression, and identification model. In addition, it includes some simple examples applied to health sciences. The second section deals with the estimation and evaluation of the model. On the first topic, the methods of Maximum Likelihood (ML), Generalized Least Squares, Unweighted Least Squares, and ML with robust standard errors are addressed, as well as alternative methods to the problem of violations of the multivariate normality assumption. On the second topic, some goodness of fit statistics of the estimated model are defined, such as the chi-square statistic, Root Mean Square Error of Approximation, Tucker-Lewis Index, Comparative Fit Index, Standardized Root Mean Square Residual, and Goodness of Fit Index. The last section deals with SEM example and its implementation using the lavaan library of R software.

**Keywords:** causal effects, path diagram, measurement model, confirmatory factor analysis, structural regression
