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

Cristina Davino (), Pasquale Dolce (), Giuseppe Lamberti () and Domenico Vistocco ()
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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

Chapter Chapter 2 in Partial Least Squares Path Modeling, 2023, pp 23-54 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-37772-3_2

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DOI: 10.1007/978-3-031-37772-3_2

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