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Quantile composite-based path modeling

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Abstract

The paper aims at introducing a quantile approach in the Partial Least Squares path modeling framework. This is a well known composite-based method for the analysis of complex phenomena measurable through a network of relationships among observed and unobserved variables. The proposal intends to enhance potentialities of the Partial Least Squares path models overcoming the classical exploration of average effects. The introduction of Quantile Regression and Correlation in the estimation phases of the model allows highlighting how and if the relationships among observed and unobserved variables change according to the explored quantile of interest. The proposed method is applied to two real datasets in the customer satisfaction measurement and in the sensory analysis framework but it proves to be useful also in other applicative contexts.

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Notes

  1. Mode A takes the first component from a PLS regression, while Mode B takes all PLS regression components.

  2. The data set is included in the R package plspm Sanchez and Trinchera (2012).

  3. http://www.theacsi.org/the-american-customer-satisfaction-index.

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Acknowledgments

The Authors wish to thank Pasquale Dolce from the University of Naples “Federico II” for his contribution in the implementation phase of QCPM and for his valuable help in running the first simulation studies.

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Authors and Affiliations

  1. Department of Political Sciences, Communications and International Relations, University of Macerata, Piazza Strambi 1, I-62100, Macerata, Italy

    Cristina Davino

  2. Department of Information Systems, Decision Sciences and Statistics, ESSEC Business School, 3, Avenue Bernard Hirsch, CS 50105, 95021, Cergy-Pontoise Cedex, France

    Vincenzo Esposito Vinzi

Authors
  1. Cristina Davino
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  2. Vincenzo Esposito Vinzi
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Correspondence to Cristina Davino.

Appendix

Appendix

See Tables 9 and 10.

Table 9 QCPM8 and PLSPM outer weights
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Table 10 QCPM8 and PLSPM outer weights
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Davino, C., Vinzi, V.E. Quantile composite-based path modeling. Adv Data Anal Classif 10, 491–520 (2016). https://doi.org/10.1007/s11634-015-0231-9

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