Tutorial: The PLS component-based approach to structural equation modeling: methodological foundations, recent developments and software applications
The component-based approach to Structural Equation Modelling (SEM) was initiated by Herman Wold under the name “PLS” (Partial Least Squares). PLS Path Modelling (PLS-PM) is generally meant as a component-based approach to structural equation modelling that privileges a prediction oriented discovery process to the statistical testing of causal hypotheses. Generally speaking, component-based SEM can be considered as a generalized data analysis approach to multiple tables connected by a network of “causal” relationships. As such, this approach is mainly used for scores computation and privileges a prediction oriented discovery process to the statistical testing of causal hypotheses. More specifically, PLS is a limited information two-step method essentially based on a set of interdependent simple and multiple OLS regressions both for the measurement and the structural model. The simplicity of the PLS algorithm makes it feasible also for (very) small samples.
A few comparisons between covariance-based and component-based SEM have shown reasons in favour of one approach or the other depending on different factors such as, for instance, the nature of the model, the research objective, the sample size, the definition of latent variables by means of reflective or formative manifest variables, the estimation and practical meaning of factor scores. With reference to component-based SEM and, specifically, to PLS Path Modelling, this tutorial will focus on the algorithms, the estimation options and process, the model assessment and interpretation. Moreover, it will sketch some of the current important research issues such as the optimization of a criterion, the measurement model misspecification, the treatment of formative relationships between manifest and latent variables, the estimation and the interpretation of scores in presence of strongly correlated latent variables.
The methodological presentation will be accompanied by applications on real data. The PLSPM module of the XLSTAT software will be used.
Lecturer: Vincenzo Vinzi
Required skills: It is preferable, but not compulsory that participants have background knowledge of regression, principal component analysis, statistical inference and confirmatory factor analysis.
Date: Friday, March 13, 9 am -1 pm
Deadline for registration: February 27, 2009
Fees: IFCS members: 50 ¤; Non-IFCS members: 100 ¤; Students: 25 ¤
No. of participants: Minimum 5
The tutorial will be given during the 2009 Conference of the International Federation of Classification Societies.