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In research, SEM typically stands for Structural Equation Modeling. Structural Equation Modeling is a statistical technique used to test and estimate causal relationships among variables. It is often employed in social sciences, psychology, economics, and other fields where researchers seek to understand complex relationships between multiple variables.

SEM allows researchers to examine both the direct and indirect effects of variables on one another within a theoretical framework. It involves specifying a set of structural equations that represent hypothesized relationships among latent (unobservable) and observed variables, and then estimating the parameters of these equations using statistical methods.

SEM can be used for various purposes in research, including:

Confirmatory Factor Analysis (CFA): Assessing the validity and reliability of measurement scales by examining the relationships between observed variables and latent constructs.
Path Analysis: Investigating direct and indirect relationships between variables to test specific hypotheses or theoretical models.
Mediation Analysis: Exploring the underlying mechanisms through which one variable influences another by examining the indirect effects of mediators.
Moderation Analysis: Examining how the strength or direction of the relationship between variables varies depending on the levels of other variables.
Structural Equation Modeling (SEM): Building and testing complex theoretical models that incorporate multiple variables.