Pls: a Silver Bullet?

We are writing this editorial because it appears to us that some researchers in the Information Systems community view partial least squares modeling (PLS; also referred to as path analysis with composites or soft modeling) as some type of magical silver bullet. These researchers are less critical about the use of PLS than they should be. In spite of cautiously proposed rules of thumb available in the PLS literature, we are frustrated by sweeping claims made by some researchers that PLS modeling can or should be used (often instead of the covariance-based approach) because it makes no sample size assumptions or because somehow “Sample size is less important in the overall model” (Falk and Miller 1992, p. 93). We are seeing an increasing number of such claims in papers submitted for review. It would be nice to think that such statements would be weeded out in the review process. However, more and more studies across a number of disciplines are creeping into the literature in which the samples are dwindling to ridiculously small sizes, despite the inferential intentions of the studies and the magnitude of parent populations. The use of small samples in these studies is frequently legitimized by references to the original developers of the PLS approach. Even MIS Quarterly has published at least one such study, which incorrectly states that “the PLS approach does not impose sample size restrictions for the underlying data.” While many MIS Quarterly readers use PLS correctly, we are writing this editorial to combat the mistaken belief held by some in the IS community that PLS may be used in all cases when the sample size is small. Rather, we wish to stress the importance of adequate sample size, as well as the related issue of standard errors, in PLS research.