What Is Structural Equation Modeling?

Structural equation modeling, or SEM, is a statistical method that examines the relation- ships among numerous variables in a simultaneous way. SEM is not considered a single procedure but rather a family of related statistical techniques. This family of analysis tech- niques examines the measurement properties of a variable along with the interrelationships between variables and is often seen as a combination of regression and factor analysis. The use of SEM will often take a confirmatory approach in which the researcher has proposed a “model” of relationships between variables of interest and examines whether the observed data will provide evidence of directionality and significance of the relationships. SEM is very similar to multiple regression but is much more robust and has greater flexibility in the analysis. SEM allows you to model multiple independent and dependent variables, error terms, interactions, and correlations. Using a SEM model will also let you denote which independent variables will influence dependent variables, and subsequently, let dependent variables be independent variables in other relationships.

Structural equation modeling is fundamentally built around the idea of modeling, or drawing a model that represents relationships between variables. This model will use symbols to represent variables, relationships between variables, and even error in your model. While drawing a model is easy to do and can help a researcher conceptualize and understand a phenomenon of interest, what we are really doing with this modeling approach is specifying mathematical equations between variables. The use of symbols makes an extremely complicated set of simultaneous mathematical equations easy for the researcher to understand. Later in this chapter, I will introduce and explain in detail the various symbols used in SEM.

The main advantages of structural equation modeling compared to other techniques are (1) it lets you analyze the influence of predictor variables on numerous dependent variables simultaneously, (2) it allows you to account for measurement error and even addresses error in predicting relationships, and (3) it is capable of testing an entire model instead of just focus- ing on individual This is in direct contrast to similar techniques like regression that can test only one dependent variable at a time, does not account for measurement error, and focuses on singular relationships instead of the collective whole.

Source: Thakkar, J.J. (2020). “Procedural Steps in Structural Equation Modelling”. In: Structural Equation Modelling. Studies in Systems, Decision and Control, vol 285. Springer, Singapore.