COMPARING THE PREDICTIVE ABILITY OF PLS AND COVARIANCE MODELS Completed Research Paper

Partial Least Squares (PLS) is a statistical technique that is widely used in the Partial Least Squares (PLS) is a popular technique for estimating structural equation models with latent variables. It is frequently perceived as an alternative to covariance analysis of such models. While its proponents recognize the shortcomings of PLS for testing explanatory models in comparison to covariance models, PLS is instead positioned as a tool for prediction and argued to be preferable to covariance analysis for this purpose. In this paper, we present an initial study that compares the predictive ability of PLS and covariance analysis in a range of situations using a simulation study. Our results show that PLS does offer some advantages over covariance models, but that these are not the ones advocated by PLS proponents