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SEM has three major advantages over traditional multivariate techniques: (1) explicit assessment of measurement error; (2) estimation of latent (unobserved) variables via observed variables; and (3) model testing where a structure can be imposed and assessed as to fit of the data.

Structural equation modeling (SEM) is commonly used to analyze such data. With a sufficient number of participants (N), SEM enables researchers to easily set up and reliably test hypothetical relationships among theoretical constructs as well as those between the constructs and their observed indicators.

SEM model specification requires researchers to support hypothesis with theory or research and specify relations a priori. Third, SEM is a multivariate technique incorporating observed (measured) and unobserved variables (latent constructs) while traditional techniques analyze only measured variables.

Structural equation modeling (SEM) is a powerful multivariate analysis technique that is widely used in the social sciences [1].

SEM analysis procedures Step 1: Specify the model. In the model specification, the researcher specifies the model by determining every relationship between variables relevant to the researcher's interest. ... Step 2: Identify the model. ... Step 3: Estimate the model. ... Step 4: Test the model fit. Step 5: Manipulate the model.

A Structural Equation Model often follows some general practices: Latent variables are denoted by a circle. Measured variables are denoted by a square. Relationships are denoted by arrows. Variances and residuals are denoted by arrows from a variable to itself.

Assess Measurement Validity: SEM enables researchers to assess the validity and reliability of measurement instruments used to collect data. By modeling the relationships between latent constructs and observed indicators, SEM allows for the evaluation of measurement errors, construct validity, and reliability.