Factor-based SEM building on consistent PLS: An information systems illustration

Partial least squares (PLS) methods have desirable characteristics that have led to their extensive use in the field of information systems for path analyses with latent variables. Such variables are typically conceptualized as factors in structural equation modeling (SEM). In spite of their desirable characteristics, PLS methods suffer from a fundamental problem: unlike the classic covariance-based SEM, they do not deal with factors, but with composites, and as such do not account for measurement error. This leads to biased parameters, even as sample sizes grow to infinity. We discuss a method that builds on the consistent PLS technique and that deals with factors, fully accounting for measurement error. We provide evidence that this new method shares the properly of statistical consistency with covariance-based SEM, but like traditional PLS methods has greater statistical power. Moreover, our method provides correlationpreserving estimates of the factors, which can be used in a variety of other tests. Our discussion builds on an illustrative model developed based on an information systems theory and related empirical work.