Path analysis: An overview and its application in social sciences

Abstract

Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It was developed by Geneticist Sewell Wright in the year 1921 and he describes path analysis as a technique based on a series of multiple regressions analysis with the added assumption of a causal relationship. Path analysis and regression analysis have similarities as path analysis is essentially the multiple regression analysis with standardized variables and the ß coefficient in regression analysis is equivalent to the test of significance of path coefficients. Path analysis was first developed as a method to decompose correlation coefficient into different components. Path analysis is mainly composed of five elements namely exogenous variables, endogenous variables, path diagram, path coefficient and effects. Path analysis assumes that there is linear relationship among the variables and all the variables are measured in interval scale. Though the methodology was used by a geneticist at the beginning, later Blalock introduced this concept into social scientific research. The algebra and tracing rules have been simplified in path analysis technique compared to conventional statistical methods so that even people with very little statistical training could perform path analysis.