SEM is a collection of statistical techniques that allow a set of relationships between one or more independent variables and one or more dependent variables to be examined. Both independent and dependent variables can be either continuous or discrete and can be either factors or measured variables. SEM also goes by several other names: causal modeling, causal analysis, simultaneous equation modeling, analysis of covariance structures, path analysis, and confirmatory factor analysis. Read more about structural equation modeling, how it's done, and see an example of a path diagram.

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