On a Composite Indicator for Observing and Monitoring Scientific Entities: Fair-E

On a Composite Indicator for Observing and Monitoring Scientific Entities: Fair-E | 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 On a Composite Indicator for Observing and Monitoring Scientific Entities: Fair-E Guilherme Sydow Nunes Bueno Brandão, Agma Juci Machado Traina This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8436778/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 The aim of this article is to present the conceptualization and statistical validation of a unidimensional composite indicator designed to monitor the performance of scientific entities, named the Flexible and Accurate Composite Indicator for Research Entities (Fair-E). A central contribution of the article is its direct response to long-standing criticisms—primarily related to statistical weakness and lack of transparency—commonly directed at composite indicators. The validation procedure addressed the specific characteristics of the dataset through multiple samples and a stress test, including the rotation of factor loadings, the factor extraction method, the use of robust estimators, and the assessment of model fit, ensuring statistical robustness and internal consistency. The absence of weighting was theoretically justified, the use of the Average Variance Extracted (AVE) was dismissed, and unidimensionality was demonstrated both statistically and conceptually. Finally, a Random Forest regression was employed as a robustness and stability check under predictive validation, rather than as evidence of causal inference. The article delivers twelve statistically valid compositions of Fair-E and reports their stability across different sample sizes, allowing application across different types of entities and research fields. The indicator model also supports informed decision-making by combining quantitative analysis, graphical representation, and qualitative interpretation, reinforcing the integration of different analytical perspectives. Scientometrics Statistics Responsible Research assessment Research entities Composite indicator unidimensionality Full Text Additional Declarations No competing interests reported. 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. 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