Path coefficients are standardized versions of
linear regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is call ...
weights which can be used in examining the possible causal linkage between
statistical variables in the
structural equation modeling
Structural equation modeling (SEM) is a label for a diverse set of methods used by scientists in both experimental and observational research across the sciences, business, and other fields. It is used most in the social and behavioral scienc ...
approach. The standardization involves multiplying the ordinary regression coefficient by the standard deviations of the corresponding explanatory variable: these can then be compared to assess the relative effects of the variables within the fitted regression model. The idea of standardization can be extended to apply to partial regression coefficients.
The term "path coefficient" derives from
Wright
Wright is an occupational surname originating in England. The term 'Wright' comes from the circa 700 AD Old English word 'wryhta' or 'wyrhta', meaning worker or shaper of wood. Later it became any occupational worker (for example, a shipwright is ...
(1921), where a particular diagram-based approach was used to consider the relations between variables in a multivariate system.
[Dodge, Y. (2003) ''The Oxford Dictionary of Statistical Terms'', OUP. ]
See also
*
Path analysis (statistics) 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, ...
Notes
References
*Shipley, B. (2000) ''Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference'' Cambridge University Press. {{isbn, 0-521-52921-2
*
Wright, S. (1921) "Correlation and causation", ''Journal of Agricultural Research'', 20, 557–585.
Structural equation models