Structural Equation Modeling (SEM) is a statistical data analysis methodology used to fit a pre-defined theoretical model to statistical problems. The comprehensive way in which structural equation modeling (SEM) operates, makes it the preferred model as compared to the regression analysis modeling technique. SEM considers a latent variable or unobserved variable and an observed variable in its modeling technique.
In addition, as opposed to regression models, SEM also considers the error testing parameter. The error testing parameter allows for necessary corrections which may be required to correct the subsequent differences between the expected value and actual value.
Unlike other statistical modeling techniques, the SEM is about creating a structure at the conceptual level and then applying the numeric equations to the pre-defined structure. SEM considers more than one dependent variable, unlike, the regression modeling technique which allows only one dependent variable.
In SEM, it is common to draw a path diagram to develop an integrated structure that connects the latent variable (unobserved variable) with the measurable or observed variable. An arrow with one pointer, that begins from the latent variable and points at an observed variable indicates variance between 2 factors; whereas, an arrow with 2 pointers which is located between 2 latent variables indicating that they are integrated, represents covariance between 2 factors.
It can be observed that Structural Equation Modeling (SEM) is a more recommended methodology to design and solve the statistical problem of a highly complex nature that includes observed variables, unobserved variables, error assimilators, and inter-connectedness among single and multiple variables.
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