When to Use PLS-SEM (and When Not)
Hair et al. (2019), p. 5: "Researchers should select PLS-SEM:
- when the analysis is concerned with testing a theoretical framework from a prediction perspective;
- when the structural model is complex and includes many constructs, indicators and/or model relationships;
- when the research objective is to better understand increasing complexity by exploring theoretical extensions of established theories (exploratory research for theory development);
- when the path model includes one or more formatively measured constructs;
- when the research consists of financial ratios or similar types of data artifacts;
- when the research is based on secondary/archival data, which may lack a comprehensive substantiation on the grounds of measurement theory;
- when a small population restricts the sample size (e.g. business-to-business research); but PLS-SEM also works very well with large sample sizes;
- when distribution issues are a concern, such as lack of normality; and
- when research requires latent variable scores for follow-up analyses."
Hair, J.F./ Risher, J.J./ Sarstedt, M./ Ringle, C.M.: When to Use and How to Report the Results of PLS-SEM, European Business Review, Volume 31 (2019), Issue 1, pp. 2-24.
Also see these articles that provide recommendations when to select PLS-SEM (and when not):
- Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, Mirror on the Wall: A Comparative Evaluation of Composite-based Structural Equation Modeling Methods. Journal of the Academy of Marketing Science (JAMS), 45(5), 616-632.
- 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.
- Richter, N. F., Cepeda Carrión, G., Roldán, J. L., & Ringle, C. M. (2016). European Management Research Using Partial Least Squares Structural Equation Modeling (PLS-SEM): Editorial. European Management Journal, 34(6), 589-597.
- Rigdon, E. E. (2016). Choosing PLS Path Modeling as Analytical Method in European Management Research: A Realist Perspective. European Management Journal, 34(6), 598-605.
- Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation Issues with PLS and CBSEM: Where the Bias Lies! Journal of Business Research, 69(10), 3998-4010.
- Sarstedt, M./ Ringle, C.M./ Hair, J.F. (2017): Partial Least Squares Structural Equation Modeling. In Homburg, C., Klarmann, M., and Vomberg, A. (Eds.), Handbook of Market Research. Heidelberg: Springer, 1-40.