[Methodology ]路徑分析法 Path Analysis

In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis of variance and covariance analyses (MANOVA, ANOVA, ANCOVA).
In addition to being thought of as a form of multiple regression focusing on causality, path analysis can be viewed as a special case of structural equation modeling (SEM) – one in which only single indicators are employed for each of the variables in the causal model. That is, path analysis is SEM with a structural model, but no measurement model. Other terms used to refer to path analysis include causal modeling, analysis of covariance structures, and latent variable models.
 (resources:http://en.wikipedia.org/wiki/Path_analysis_%28statistics%29)    


Historical Background
Path analysis was developed as a method of decomposing correlations into different pieces for interpretation of effects (e.g., how does parental education influence children's income 40 years later?). Path analysis is closely related to multiple regression; you might say that regression is a special case of path analysis. Some people call this stuff (path analysis and related techniques) "causal modeling." The reason for this name is that the techniques allow us to test theoretical propositions about cause and effect without manipulating variables. However, the "causal" in "causal modeling" refers to an assumption of the model rather than a property of the output or consequence of the technique. That is, people assume some variables are causally related, and test propositions about them using the techniques. If the propositions are supported, it does NOT prove that the causal assumptions are correct.
(resources:http://luna.cas.usf.edu/~mbrannic/files/regression/Pathan.html)




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