Structural equation modeling, or SEM, is a very general, chiefly linear, chiefly cross-sectional statistical modeling technique. SEM is a largely confirmatory, rather than exploratory, technique.
Measurement is recognized as difficult and error-prone.
By explicitly modeling measurement error, SEM users seek to derive unbiased estimates for the relations between latent constructs. To this end, SEM allows multiple measures to be associated with a single latent construct. If the two matrices are consistent with one another, then the structural equation model can be considered a plausible explanation for relations between the measures. Compared to regression and factor analysis, SEM is a relatively young field, having its roots in papers that appeared only in the late 1960s. As such, the methodology is still developing, and even fundamental concepts are subject to challenge and revision.