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[PRT17] Integrating non-metric data in Partial Least Squares Path Models: methods and application
Chapitres de Livre : Titre du livre: " Recent developments in partial least squares structural equation modeling: Basic concepts, methodological issues and applications", April 2017, Springer Verlag,Mots clés: PLS-PM, Non-Metric PLS-PM, Logistic Regression
Résumé:
In this chapter we discuss how to include non-metric variables (i.e. ordinal and/or nominal variables) in a PLS Path Model.We present the Non-Metric PLS approach for handling these type of variables, and we integrate the logistic regression into the PLS Path model for predicting binary outcomes. We discuss features and properties of these PLS Path Modeling enhancements via an application on real data. We use data collected by merging the archives of the Sapienza University of Rome and the Italian Ministry of Labor and Social Policy. The analysis of this data measured quantitatively, for the first time in Italy, the impact of graduates’ Educational Performance on the first three years of their job career.
BibTeX
| @inbook { | |||
| PRT17, | |||
| title | = | "{ Recent developments in partial least squares structural equation modeling: Basic concepts, methodological issues and applications}", | |
| chapter | = | "{Integrating non-metric data in Partial Least Squares Path Models: methods and application}", | |
| author | = | "F. Petrarca and G. Russolillo and L. Trinchera", | |
| year | = | 2017, | |
| publisher | = | "Springer Verlag", | |
| } | |||
