New paper on PLS-SEM
The paper “Estimation Issues with PLS and CBSEM: Where the Bias Lies,” authored by Marko Sarstedt, Joe F. Hair (University of South Alabama), Christian M. Ringle, Kai Oliver Thiele (both Hamburg University of Technology) and Siegfried P. Gudergan (University of Newcastle) has been accepted for publication in Journal of Business Research. In this paper, the authors develop a framework for model building and estimation when using PLS-SEM that aligns different measurement and estimation perspectives. Results from a simulation study substantiate the conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data’s nature is common factor- or composite-based. The full paper can be downloaded free of charge here.