Structural Equation Models: The Basics

Structural equation modeling (SEM) is a statistical methodology that takes a \nconfirmatory (i.e., hypothesis-testing) approach to the multivariate analysis \nof a structural theory bearing on some phenomenon. Typically, this theory \nrepresents "causal" processes that generate observations on multiple variables \n(Bentler, 1988). The term structural equation modeling conveys two \nimportant aspects of the procedure: (a) that the causal processes under study \nare represented by a series of structural (i.e., regression) equations, and (b) \nthat these structural relations can be modeled pictorially to enable a clearer \nconceptualization of the theory under study. The hypothesized model can \nthen be tested statistically in a simultaneous analysis of the entire system of \nvariables to determine the extent to which it is consistent with the data. If \ngoodness-of-fit is adequate, the model argues for the plausibility of postulated \nrelations among variables; if it is inadequate, the tenability of such relations \nis rejected.