Robustification of structural equation modelling via global sensitivity analysis

Alessio Lachi (), Josep Llach (), Jordi Perramon (), Michela Baccini () and Andrea Saltelli
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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

Statistical Methods & Applications, 2025, vol. 34, issue 2, No 3, 236 pages

Abstract: 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.

Keywords: Structural equations modelling; Global sensitivity analysis; Uncertainty modelling; Uncertainty quantification; Sobol indexes; Robustification; Bootstrap (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10260-025-00783-3

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