I have never used SEM in the past 30 professional years as a statistician. I have never encountered a statistics course in which SEM is being taught. SEM is being widely used in some disciplines, though. I do not know much about it.

Hello,

Here are some references comparing SEM to regression that you may find informative:

Cheng, E. W. (2001). SEM being more effective than multiple regression in parsimonious model testing for management development research. Journal of management development, 20(7), 650-667.

Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7.

Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 140-154.

Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5(6), 359-371.

Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.

We often use structural equation modeling (SEM) to test any framework. Why?

SEM (Covariance-based & Variance-based) is 2nd generation data analysis technique which is more powerful in modeling relationships among multiple independent and dependent variables / constructs simultaneously vs 1st generation data analysis techniques like linear regression, ANOVA & MANOVA whereby the latter is analyzing the relationship between dependent & independent variables one at a time.  So depending on your research requirement, conceptual framework, research model, hypotheses etc., you can adopt the appropriate data analysis techniques according to your particular needs.  E.g. if your research model is a complex one that consists of multiple relationships among independent & dependent variables, you can explore SEM.  If your research requirement is to compare differences among groups or less complex model, you can adopt e.g. ANOVA, multiple linear regression etc.