Structural equation modeling is a statistical technique for testing
and estimating causal relations using a combination of statistical
data and qualitative causal assumptions.
This definition of SEM was articulated by the geneticist Sewall Wright,
the economist Trygve Haavelmo and the cognitive scientist Herbert A.
Simon, and formally defined by Judea Pearl using a calculus of counterfactuals.
According to Hoyle, Structural equation modeling may also be explained as
a comprehensive statistical approach to testing hypotheses exploring
relations between observed and latent variables. It is a methodology
for representing, estimating, and testing a theoretical network of
(mostly) linear relations between variables (Rigdon, 1998).
Structural Equation Modeling Has two main goals:
(i) To understand the patterns of correlation/covariance among a set of variables and
(ii) Explaining as much of their variance as possible with the model specified
Source: https://www.digitalvidya.com/blog/structural-equation-modeling/