Structural Equation Modelling: A Powerful Antibiotic


  • Delhi School of Economics, University of Delhi, Department of Commerce, Delhi, 110021, India


This article is an attempt to scrutinize the applicability of the widely used statistical technique of Structural Equation Modelling (SEM). SEM is a comprehensive technique to test the model adequacy. SEM is considered as an important advancement in social science research as it combines measurement with substantive theories. It has been observed that many studies pay attention to statistical mechanisation of SEM rather than the assumptions on which it is based. The history of SEM can be traced to Regression Analysis, Path Analysis and Confirmatory Factor Analysis. SEM is popularly applied because of its use in estimating multiple dependence relationships. It is able to measure the unobserved variables, define the model representing the set of relationships and also corrects the measurement error. The technique is commonly applied in disciplines including sociology, psychology and other fields of behavioural science. The availability of various user-friendly software programmes like LISREL, AMOS, EQS, Mx, Mplus and PISTE is an added advantage. However, one should be careful while using SEM for causal inferences. In comparison to other common standard statistical techniques, SEM is based on several assumptions. The technique requires a priori knowledge of all the parameters to be estimated and a substantial amount of data pertaining to covariances, variances and path coefficients. It also requires relationships to be specified in the model. The model inherently assumes temporal precedence and is heavily dependent on researcher’s judgements about exogeneity and directionality. Normality is yet another important assumption of SEM. The mismatch between data characteristics and assumptions imperils inference and accuracy. Like antibiotics are a boon to mankind yet one needs to judiciously use them. Similarly, SEM is a powerful technique however, researchers are suggested to apply cautiously.


Confirmatory Factor Analysis, Latent Variables, Path Analysis, Regression Analysis, Structural Equation Modelling

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