How can researchers choose between covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM) approaches, and what are the main differences between these methods?
The normality of data distribution and sample size are the factors that affect the choice of tools. Use PLS. Because the normality or non-normality of the data distribution will not affect the function of the software. The small number of samples does not have a very important effect. Of course, the more the number of samples, the better, but the small number of samples will not be a critical problem.
For more information on the differences between the two methods, refer to the :A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)," by Hair, Hult, Ringle, and Sarstedt.
Here's another paper by somewhat the same authors, in which they discuss differences between CB-SEM and PLS-SEM. And as in many cases, it depends ... ;-)
Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations. Marketing ZFP, 39(3), 4-16.
Hi, simply avoid PLS (and please stop calling it PLS-SEM). Latent variables in the PLS context are simple composites (and not real latent variables) and can be created by simple additive sums. Further, a SEM allows to test causally based restrictions (comparable to the exclusion restrictions in econometrics) which allow to test your model and to differentiate it from other alternatives. PLS is a form of regression with all its disadvantages.
All the best,
Holger
McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17(2), 210-251. https://doi.org/10.1177/1094428114529165
Rönkkö, M., Lee, N., Evermann, J., McIntosh, C., & Antonakis, J. (2023). Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations. European Journal of Marketing.
Rönkkö, M., McIntosh, C. N., & Antonakis, J. (2015). On the adoption of partial least squares in psychological research: Caveat emptor. Personality and Individual Differences, 87, 76-84.
Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47, 9-27. https://doi.org/10.1016/j.jom.2016.05.002
Rouse, A., & Corbitt, B. (2008). There's SEM and "SEM": A critique of the use of PLS regression in information systems research. ACIS 2008 Proceedings, 81.
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