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Statistics Spotlight: Structural Equation Modeling

By December 23, 2011

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Structural equation modeling is an advanced statistical technique that has many layers and many complex concepts. The purpose of structural equation modeling, or SEM for short, is to attempt to explain "raw" correlations among directly observed variables. Many know SEM by the unique diagrams containing ovals, squares, and arrows, called path diagrams.

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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