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In statistics, path analysis is a type of multiple regression analysis. The term path analysis has been used to refer to the analysis of causal models when single indicators are employed for each of the variables in the model. Other terms used to refer to this model are causal modeling, analysis of covariance structures, latent variable models, structural modeling, and structural equation modeling. In the hypothetical model below, the two exogenous variables are taken as correlated and are shown to have direct as well as indirect effects (through En1) on En2.

Path example.JPG

Using the same variables, alternative models are conceivable. For example, it may be hypothesized that Ex1 has only an indirect effect on En2, thus the arrow from to Ex1 to En2 would be deleted. It may be that the endogenous variables (dependent variables) are also affected by variables other than the identified exogenous variables (independent variables) and thus not in the model. The effects of such extraneous variables are depicted by the various e’s in the figure.

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