Partial Least Squares (Small-Sample Alternative to Conventional SEM)

Partial Least Squares (PLS) is a variation on Structural Equation Modeling (SEM). Riou, Guyon, and Falissard (2016) state that, relative to conventional SEM, PLS “is more suitable to … work with smaller sample sizes.” PLS is recommended for exploratory purposes, and is often used with single-indicator constructs. The technique seems to be used predominantly within the field of Management Information Systems (MIS).

Significance testing is done through bootstrapping, with 100 random variations of the original data set being generated and the model rerun in each random data set. An actual path coefficient from one’s model can then be evaluated for extremity, relative to the distribution of the same coefficient estimated 100 times from the bootstrap.

Though PLS may have reputation for making it easier to obtain significant results, this view appears overstated; a simulation study found that “for N = 40, PLS had 3% and 1% higher power than regression for strong and medium effect sizes [and…] the same power as regression at weak effect size” (Goodhue, Lewis, & Thompson, 2006).

Fit indices, such as NFI, CFI, RMSEA, are not available.

WarpPLS (Kock, 2015) is a program I've found useful and that has a three-month free trial version. Note that the probabilities given in WarpPLS output are one-tailed, so that if you want to report two-tailed p-values, you must double the printed value (e.g., p = .02 one-tailed represents p = .04 two-tailed).

Discussion of the pros and cons of PLS, and of the circumstances for which it may -- or may not -- be appropriate, is available in Goodhue, Thompson, and Lewis (2013); Marcoulides, Chin, and Saunders (2009); McIntosh, Edwards, and Antonakis (2014); and other sources. See also this discussion piece by Kock.

References

Goodhue, D., Lewis, W., & Thompson, R. 2006. “PLS, small sample size and statistical power in MIS research,” in Proceedings of the 39th Hawaii International Conference on System Sciences, R. Sprague Jr. (ed.), Los Alamitos, CA: IEEE Computer Society Press. (link)

Goodhue D. L., Thompson R. L., & Lewis W. (2013). Why you shouldn’t use PLS: Four reasons to be uneasy about using PLS in analyzing path models. In 46th Hawaii International Conference on System Sciences (pp. 4739–4748). Wailea, HI: HICSS.

Kock, N. (2015). WarpPLS 5.0 User Manual. Laredo, TX: ScriptWarp Systems. (link)

Marcoulides, G. A., Chin, W. W., & Saunders, C. (2009). A critical look at partial least squares modeling. MIS Quarterly, 33(1), 171-175. (link)

McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17, 210-251. (abstract)

Riou, J., Guyon, H., & Falissard, B. (2016). An introduction to the partial least squares approach to structural equation modelling: A method for exploratory psychiatric research. International Journal of Methods in Psychiatric Research, 25, 220-231. Published online first at doi: 10.1002/mpr.1497.