PLS-SEM with factor estimation (PLSF-SEM): An applied discussion in the field of marketing

The article below (mentioned as forthcoming in a recent post; now published!) explains how one can conduct a factor-based PLS structural equation modeling (PLSF-SEM) analysis, with an illustration in the field of marketing, as well as the advantages of using PLSF-SEM in terms of avoidance of type I and II errors.

Kock, N. (2019). Factor-based structural equation modeling with WarpPLS. Australasian Marketing Journal, 27(1), 57-63.

A link to a PDF file is available ().

Abstract:

Structural equation modeling (SEM) is extensively used in marketing research. For various years now, there has been a somewhat heated debate among proponents and detractors of the use of the partial least squares (PLS) method for SEM. The classic PLS design, originally proposed by Herman Wold, has a number of advantages over covariance-based SEM; e.g., minimal model identification demands. However, that design does not base its model parameter recovery approach on the estimation of factors, but on composites, which are exact linear combinations of indicators. This leads to adverse consequences, primarily in the form of unacceptable levels of type I and II errors. Recently a new factor-based method for SEM has been developed, called PLSF, which we discuss in this paper. This method has the advantages of classic PLS, but without the problems inherent in the use of composites. For readers interested in trying it, the PLSF method is implemented in the SEM software WarpPLS.