Structural equation modeling (SEM) is a multivariate statistical analysis technique, utilized to analyze complex patterns of relationships among a set of variables, conduct between-groups and within-groups comparisons, and validate theoretical and empirical models. SEM can be used to answer several research questions including those formulated in the context of experimental and non-experimental designs. Despite the several advantages that SEM has over traditional procedures, such as multiple regression or ANOVA, it has not been applied frequently in the medical and health science domains. Therefore, the purpose of this article is to present SEM as a robust and comprehensive analytical technique capable of strengthening and increasing the accuracy of the analyses in medical and health research. The functioning and applications of SEM are illustrated through one research example: a Chilean study of psychological predictors of obesity and metabolic syndrome. This article is aimed at contributing to a better understanding of this technique among readers of scientific publications and facilitating its implementation in future research