Posted April 2, 2012 by RhoBeta in Analytics

What is Path Analysis?

Path Analysis
Path Analysis

About Path Analysis

Path analysis can be defined as a statistical technique which makes use of multiple regression to test causal relationships between variables. It is used not only to test causal relationships but also to ask questions about the minimum number of relationships (causal influences) and their directions; this is done by observing the sizes of the regression coefficients with and without certain variables entered into equations. Goal of path analysis is to provide plausible explanations of observed correlations by constructing models of cause-and-effect relations.

Path analysis makes use of multiple regression analysis to look at the relationship between variables. The primary difference between the techniques is that path analysis graphically and explicitly looks at causal factors. The relationships between variables are designated by path coefficients (the standard regression coefficients from multiple regressions) and show the effect of the independent on the dependent variables and also any relationships between independent variables.


Path analysis became an important tool in population genetics, economics and, from the 1960s onward, the social sciences. Lately the technique has been applied to political trends and it can be used in any situation where regression would be used. Path analysis is little more than the graphic illustration of multiple regression. It is not a complicated technique. It is the graphic illustration, however, that adds richness to the data as it emerges through the diagramming process and leads to an end product that can clearly show relationships, strengths and directions.

Use of Path Analysis

Path analysis is used when you have complex relationships between variables that may not be adequately examined using multiple regression analysis. We can use path analysis when we develop a hypothesis about the causal relationships between variables, so it is applied when we are examining the sorts of relationships investigated using regression with more than one independent variable. There are, however, several crucial differences between path analysis and regression. The first is that there can be more than one dependent variable. The second is that the correlations between independent variables can be more readily accessed or identified. Path analysis is valuable when a visual representation of data aids in understanding the relationships it contains as it helps to identify and assess the significance of competing causal pathways.


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