Authors: Ned Kock
Addresses: Division of International Business and Technology Studies, Texas A&M International University, Laredo, TX, 78041, USA
Abstract: Partial least squares (PLS) methods offer many advantages for path modelling, such as fast convergence to solutions and relaxed requirements in terms of sample size and multivariate normality. However, they do not deal with factors, but with composites. As a result, they typically underestimate path coefficients and overestimate loadings. Given these, it is difficult to fully justify their use for confirmatory factor analyses or factor-based structural equation modelling (SEM). We addressed this problem through the development of a new method that generates estimates of the true composites and factors, potentially placing researchers in a position where they can obtain consistent estimates of a wide range of model parameters in SEM analyses. A Monte Carlo experiment suggests that this new method represents a solid step in the direction of achieving this ambitious goal.
Keywords: partial least squares; PLS; structural equation modelling; measurement error; path bias; variation sharing; Monte Carlo simulation.
International Journal of Data Analysis Techniques and Strategies, 2019 Vol.11 No.1, pp.1 - 28
Available online: 29 Oct 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article