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.
SEM Systems of Composite Indicators Categorical Variables
Laura Trinchera Giorgio Russolillo
University degli Studi di Macerata, Macerata, Italy Universita degli Studi di Napoli Federico II, Napoli, Italy
国际会议
The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)
北京
英文
23-27
2009-09-04（万方平台首次上网日期，不代表论文的发表时间）