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

Francesca Petrarca (), Giorgio Russolillo () and Laura Trinchera ()
Additional contact information
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

Chapter Chapter 12 in Partial Least Squares Path Modeling, 2017, pp 259-279 from Springer

Abstract: 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.

Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-64069-3_12

Ordering information: This item can be ordered from
http://www.springer.com/9783319640693

DOI: 10.1007/978-3-319-64069-3_12

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().