Role and Treatment of Categorical Variables in PLS Path Models for Composite Indicators

Nowadays there is a pre-eminent need to measure very complex phenomena like poverty, progress, well-being, etc. As is well known, the main feature of a composite indicator is that it summarizes complex and multidimensional issues. Thanks to its features, Structural Equation Modeling seems to be a useful tool for building systems of composite indicators. Among the several methods that have been developed to estimate Structural. Equation Models we focus on the PLS Path Modeling approach (PLS-PM), because of the key role that estimation of the latent variables (i.e. the composite indicators) plays in the estimation process. In this paper we provide a suite of statistical methodologies for handling categorical indicators with respect to the role they have in a system of composite indicators.