Statistica Applicata Vol. 20, n. 3-4, 2008 309 USING CATEGORICAL VARIABLES IN PLS PATH MODELING TO BUILD SYSTEM OF COMPOSITE INDICATORS 1 Laura Trinchera Dipartimento di Studi sullo Sviluppo Economico, Università degli Studi di Macerata, Piazza Oberdan, 3 – Macerata (Italy) laura.trinchera@unimc.it Giorgio Russolillo, Carlo N. Lauro Dipartimento di Matematica e Statistica, Università degli Studi di Napoli “Federico II”, Via Cintia – Complesso Monte S. Angelo – Napoli (Italy) giorgio.russolillo@unina.it Abstract 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. A categorical variable can play an active or a moderating role. An active categorical variable directly participates in the construction of the model. In other words, it is a categorical indicator impacting on a composite indicator jointly with other manifest variables. A moderating categorical variable, instead, is a variable that does not play a direct role in the construction of the system of composite indicators but affects the relations, 1 This paper was financially supported by the MURST grant “Multivariate statistical models for the ex-ante and the ex-post analysis of regulatory impact”, coordinated by C.N. Lauro (2006)