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Integrating Non-metric Data in Partial Least Squares Path Models: Methods and Application

In: Partial Least Squares Path Modeling

Author

Listed:
  • Francesca Petrarca

    (Sapienza University of Rome, Department of Methods and Models for Economics, Territory and Finance)

  • Giorgio Russolillo

    (CNAM - Laboratoire Cédric)

  • Laura Trinchera

    (NEOMA Business School)

Abstract

In this chapter we discuss how to include non-metric variables (i.e., ordinal and/or nominal) 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 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 3 years of their job career.

Suggested Citation

  • Francesca Petrarca & Giorgio Russolillo & Laura Trinchera, 2017. "Integrating Non-metric Data in Partial Least Squares Path Models: Methods and Application," Springer Books, in: Hengky Latan & Richard Noonan (ed.), Partial Least Squares Path Modeling, chapter 0, pages 259-279, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64069-3_12
    DOI: 10.1007/978-3-319-64069-3_12
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