Component-Based Structural Equation Modeling for Small Samples: A Comparison Between PLS, GSCA, and ULS-SEM

• Activity Number: 471
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2008 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and Marketing
• Author(s): Michel Tenenhaus*+
• Companies: HEC Paris
• Keywords: Structural Equation Modelling ; PLS path modelling ; Multi-block analysis

Abstract

Two competing schools have come to the fore in the field of Structural Equation Modeling (SEM): covariance-based SEM and component-based SEM. The first approach has been developed around Karl Jöreskog and the second one around Herman Wold under the name "PLS" (Partial Least Squares). Hwang and Takane have proposed a new component-based SEM method named Generalized Structural Component Analysis. Covariance-based SEM is usually used with an objective of model validation and needs a large sample. Component-based SEM is mainly used for score computation and can be carried out on very small samples. In this research, we will explore the use of ULS-SEM, PLS, and GSCA on small samples. First experiences have shown that score computation and bootstrap validation are very insensitive to the choice of the method. We will also study the contribution of these methods for multiblock analysis.