Robustification of Structural Equation Modelling via Global Sensitivity Analysis

Robustification of Structural Equation Modelling via Global Sensitivity Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robustification of Structural Equation Modelling via Global Sensitivity Analysis Alessio Lachi, Josep Llach, Jordi Perramon, Michela Baccini, Andrea Saltelli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4328011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We propose a method for enhancing the robustness of structural equation modelling, a mul- tivariate statistical analysis technique employed for analyzing causal relationships among different aspects of given phenomena. This enhancement is achieved through the integra- tion 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 introduces a novel approach termed “modelling of the mod- elling process”. To illustrate this approach, we apply it to a previously published test case where structural equation modelling is used to relate the impact of artificial intelligence adoption on employee engagement. By quantifying the uncertainty inherent in the infer- ence from our test case, this procedure generates a more robust and defensible inference. The performed procedure significantly enhances the robustness of the results derived from the test case. Structural equations modelling Global sensitivity analysis Uncertainty modelling Uncertainty quantification Sobol indexes Robustification Bootstrap Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4328011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295718195,"identity":"3f38ee96-d473-41f2-9341-5605bd83af85","order_by":0,"name":"Alessio Lachi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3PsYrCMBjA8a8Evi7BrDmE+gpxqYN9mJaD3iIiuDg4VIQ+hehb3OzxgS73EFeETrcUlwwOJqWLg6mjQ/7D1wT6IwmAz/eOhXYogAEAQroyk/0UkLoI6wi25NdMzAxxGdZ9WxKUdmV/dxCxZdToRQIYUv1X7Wk+QFZApZ8TSZgPucoBeT5R2TctEYOeixGPGSgyt5qhNCQrRxs3GZG4NtoS8W/IzpC+UxRxkNwSaU8pXiBjwti+haOsY5mevlpyTPPnJDpvL42+JZEQn/WHXk+zQxlWlU4cz+/iD7tjP/D5fD6fqztmgUa/qtBaDgAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Alessio","middleName":"","lastName":"Lachi","suffix":""},{"id":295718196,"identity":"b0952113-fccd-48f3-86fb-488bdceb1ab1","order_by":1,"name":"Josep Llach","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"","lastName":"Llach","suffix":""},{"id":295718198,"identity":"11778822-d7df-493f-bd8e-74fb4f64878b","order_by":2,"name":"Jordi Perramon","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jordi","middleName":"","lastName":"Perramon","suffix":""},{"id":295718199,"identity":"218049ee-978b-4362-9b8f-a0795c5ea9eb","order_by":3,"name":"Michela Baccini","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Michela","middleName":"","lastName":"Baccini","suffix":""},{"id":295718200,"identity":"94a2127d-9e83-4e8a-afb0-cef1e44a825a","order_by":4,"name":"Andrea Saltelli","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Saltelli","suffix":""}],"badges":[],"createdAt":"2024-04-26 07:41:25","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4328011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4328011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55475726,"identity":"955ea5f3-cab2-4960-9299-273b0b94ddbd","added_by":"auto","created_at":"2024-04-29 02:18:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":225501,"visible":true,"origin":"","legend":"","description":"","filename":"SEMGSA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4328011/v1_covered_9e04b2b5-cfa7-4ba9-8d4a-2021c55a016e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRobustification of Structural Equation Modelling via Global Sensitivity Analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Università degli studi di Firenze","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Structural equations modelling, Global sensitivity analysis, Uncertainty modelling, Uncertainty quantification, Sobol indexes, Robustification, Bootstrap","lastPublishedDoi":"10.21203/rs.3.rs-4328011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4328011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe propose a method for enhancing the robustness of structural equation modelling, a mul- tivariate statistical analysis technique employed for analyzing causal relationships among different aspects of given phenomena. 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