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Non-symmetrical composite-based path modeling

Author

Listed:
  • Pasquale Dolce

    (University of Naples Federico II)

  • Vincenzo Esposito Vinzi

    (ESSEC Business School of Paris)

  • Natale Carlo Lauro

    (University of Naples Federico II)

Abstract

Partial least squares path modeling presents some inconsistencies in terms of coherence with the predictive directions specified in the inner model (i.e. the path directions), because the directions of the links in the inner model are not taken into account in the iterative algorithm. In fact, the procedure amplifies interdependence among blocks and fails to distinguish between dependent and explanatory blocks. The method proposed in this paper takes into account and respects the specified path directions, with the aim of improving the predictive ability of the model and to maintain the hypothesized theoretical inner model. To highlight its properties, the proposed method is compared to the classical PLS path modeling in terms of explained variability, predictive relevance and interpretation using artificial data through a real data application. A further development of the method allows to treat multi-dimensional blocks in composite-based path modeling.

Suggested Citation

  • Pasquale Dolce & Vincenzo Esposito Vinzi & Natale Carlo Lauro, 2018. "Non-symmetrical composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 759-784, September.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:3:d:10.1007_s11634-017-0302-1
    DOI: 10.1007/s11634-017-0302-1
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    1. Mohamed Hanafi & Pasquale Dolce & Zouhair El Hadri, 2021. "Generalized properties for Hanafi–Wold’s procedure in partial least squares path modeling," Computational Statistics, Springer, vol. 36(1), pages 603-614, March.
    2. Cristina Davino & Pasquale Dolce & Stefania Taralli & Domenico Vistocco, 2022. "Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 907-936, June.
    3. Rosaria Romano & Francesco Palumbo, 2021. "Partial possibilistic regression path modeling: handling uncertainty in path modeling," Computational Statistics, Springer, vol. 36(1), pages 615-639, March.

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