Structural Equation Modeling (SEM) is a statistical technique used to analyze complex relationships between observed and latent (unobserved) variables. It combines elements of factor analysis and multiple regression, enabling researchers to test theoretical models that include direct and indirect relationships among variables. SEM allows for the modeling of both measurement and structural components, making it ideal for testing hypotheses involving multiple variables and pathways. The technique is commonly applied in social sciences, psychology, and education research to examine models of behavior, attitudes, and performance. SEM is especially powerful because it can account for measurement error and handle both endogenous and exogenous variables. Software like AMOS, LISREL, and Mplus are typically used to conduct SEM analyses, facilitating the estimation of model parameters and the evaluation of model fit.
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