Structural Equation Modeling (SEM) is basically a multivariate statistical analysis, profoundly linear and cross-sectional modeling which includes focussed dimensions of other analysis and methods such as canonical correlation, principal component analysis, factor analysis, path analysis, multiple regression, discriminant function analysis, growth analysis, trend analysis, bi-variate and inter-correlations as special statuses. Structural equation modeling (SEM) relies on sequence of other statistical methods that infer composite relationships between independent variables and dependent variables.

Structural Equation Modeling is often referred as simultaneous equation modeling, analysis of covariance structures or casual modeling or casual analysis or path analysis or confirmatory factor analysis or analysis of moments, because of its varied applications at abstract instances where multivariate techniques may be applied. Educational research, significant as any other research area with several complex variables, dimensions and structures, proportions and magnitudes, more importantly involving social samples that make studies still abstract in all aspects require numerous analyses to arrive at an apparent logical supposition (Maccallum, 1989). Structural Equation Modeling (SEM) will create possibilities to find out causal relationships by systematic experimentations, assumptions and suppositions by means of multivariate statistical analysis hence contributing in numerous ways to derive unbiased estimates for the relations between latent constructs between the independent and dependent variables.