Content Disclaimer
Copyright @2020.
All Rights Reserved.
StatsToDo : Path Analysis Explained

Links : Home Index (Subjects) Contact StatsToDo

Related link :
Multiple Regression Explained Page
Path Analysis Program Page

Introduction Example References
Path analysis from the Path Analysis Program Page is an extension of the multiple correlation analysis described in the Multiple Regression Explained Page .

It is a method of describing complex sequential relationship between measurements. It was first used in population genetics to describe the contributions from multiple influences on attributes of a target population, such as the influences of the parents genetic characteristics and the environment on some attribute of the child. More recently the method has been found to be useful in sociological and epidemiological studies.

Conceptually, the variables (measurements) used in the model are assumed to be all the measurements that matters. These are assigned to different levels in a sequence or cascade of influences, where the earlier levels affect the subsequent ones, but the reverse does not happen. In this sequence, all measurements in prior levels affect all subsequent levels, and the scale of the influence is described by the path coefficient, and partial correlation between measurements in the same level describes the size of the as yet unexplained common preceding influences.

Mathematically, path analysis consists of a repeated sequence of multiple correlation calculations from a correlation matrix, following the cascade of influences. This is carried out one level at a time, using all the measurements in preceding levels as independent variables, and the Standardised Partial Regression Coefficients (Path Coefficients) represents the size of each influence. This is followed by calculating Partial Correlation Coefficients between all the variables in the same level, corrected for all preceding variables as well. These Partial Correlation Coefficients represents common influences that has not as yet been explained by the model.