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Robustification of structural equation modelling via global sensitivity analysis

Author

Listed:
  • Alessio Lachi

    (Saint Camillus International University of Health and Medical Sciences)

  • Josep Llach

    (University Pompeu Fabra of Barcelona)

  • Jordi Perramon

    (University Pompeu Fabra of Barcelona)

  • Michela Baccini

    (University of Florence)

  • Andrea Saltelli

    (University Pompeu Fabra of Barcelona)

Abstract

We propose a method for enhancing the robustness of Structural Equation Modelling (SEM), a multivariate statistical analysis technique employed for analyzing causal relationships among different aspects of given phenomena. This enhancement is achieved through the integration of Global Sensitivity Analysis, which assesses how uncertainties in model output can be attributed to various sources of input uncertainty. The robustification process involves several key steps, including bootstrapping evidence, error propagation, and uncertainty quantification. This method extends the approach named in the literature “modeling of the modelling process”. To illustrate this approach, we apply it to two previously published test cases where SEM is used. The first one is related to the impact of artificial intelligence adoption on employee engagement and the second one investigates the effects of service quality and environmental practices on the competitiveness and financial performance of hotels. By quantifying the uncertainty inherent in the inference of our test cases, this procedure increases the robustness of the results derived from the test cases, thus generating a more defensible inference.

Suggested Citation

  • Alessio Lachi & Josep Llach & Jordi Perramon & Michela Baccini & Andrea Saltelli, 2025. "Robustification of structural equation modelling via global sensitivity analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(2), pages 211-236, May.
    • Handle: RePEc:spr:stmapp:v:34:y:2025:i:2:d:10.1007_s10260-025-00783-3
    DOI: 10.1007/s10260-025-00783-3
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