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Quantile Composite-Based Path Modeling with R: A Hands-on Guide

In: Partial Least Squares Path Modeling

Author

Listed:
  • Cristina Davino

    (Department of Economics and Statistics, University of Naples Federico II)

  • Pasquale Dolce

    (Department of Public Health, University of Naples Federico II)

  • Giuseppe Lamberti

    (Department of Economics and Statistics, University of Naples Federico II)

  • Domenico Vistocco

    (Department of Political Science, University of Naples Federico II)

Abstract

The aim of the chapter is to provide step-by-step instructions to implement, estimate, and interpret a Quantile Composite-based Path Model, exploiting the qcpm package ( https://rdrr.io/cran/qcpm/ ), freely available for the R software. The chapter encompasses both methodological aspects of this recent quantile approach to Partial Least Squares Path Modeling, and real data applications, so as to offer a comprehensive guide to the readers interested in the use of the method on their own data. All steps of a quantitative analysis, i.e., data loadingLoadings, pre-processing, coefficient estimation and model validation are described showing the options and functionalities of the package along with the corresponding methodology.

Suggested Citation

  • Cristina Davino & Pasquale Dolce & Giuseppe Lamberti & Domenico Vistocco, 2023. "Quantile Composite-Based Path Modeling with R: A Hands-on Guide," Springer Books, in: Hengky Latan & Joseph F. Hair, Jr. & Richard Noonan (ed.), Partial Least Squares Path Modeling, edition 2, chapter 0, pages 23-54, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-37772-3_2
    DOI: 10.1007/978-3-031-37772-3_2
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