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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.

SEM involves a model representing how various aspects of some phenomenon are thought to causally connect to one another. Structural equation models often contain postulated causal connections among some latent variables (variables thought to exist but which can't be directly observed).

Structural equation modeling is a linear model framework that models both simultaneous regression equations with latent variables. Models such as linear regression, multivariate regression, path analysis, confirmatory factor analysis, and structural regression can be thought of as special cases of SEM.

Structural equation modeling is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs.

Structural equation modeling (SEM) is a methodology for representing, estimating, and testing a network of relationships between variables (measured variables and latent constructs).

How to report SEM results? To report SEM results, you should follow the APA style guidelines and provide a clear and concise summary of your model and findings. You should include a diagram or a table that shows the model specification, the parameter estimates, and the fit indices.

The purpose of structural equation modeling (SEM) is to define a theoretical causal model consisting of a set of predicted covariances between variables and then test whether it is plausible when compared to the observed data (Joreskog, 1970; Wright, 1934).