Statistical evaluation systems at 360°: techniques, technologies and new frontiers

This Special Issue has drawn inspiration from the Conference “Innovation and Society 2019—Statistical evaluation systems at 360°: techniques, technologies and new frontiers” (IES2019), which represents the 9th Scientific Meeting of the Statistics for the Evaluation and Quality of Services Group of the Italian Statistical Society—(SVQS), organized on 4 and 5 July 2019 at the “Università Europea di Roma” (Italy). This Conference was held with the aim of strengthening statistical research on evaluation topics, with a particular focus on economic and social issues, and highlighting how statistical thinking, design and analysis play a crucial role in social life, as well as how useful they are to society as a whole. To make the IES2019 conference as effective and constructive as possible, participation was open not only to scholars from different disciplines, but also to experts and decision-makers, who dealt with the relationship among evaluation, innovation and social issues. The accomplishment of this issue aims at covering an extensive array of topics on evaluation systems of the public sector through statistical methods and models. Our goal was to provide a comprehensive technical, theoretical and practical description of a variety of statistical methodologies on evaluation issue. The 11 articles of this Special Issue, selected after double-blind peer reviews, concern scientific studies applied in several fields, using many different statistical approaches and sharing the common aim stated by the Conference IES2019. From an empirical point of view, the articles deal with topics such as Education (Camminatiello et al., Cavicchia and Sarnacchiaro); Transport (D’Ambra et al., Iannario and Monti), Policy evaluation (De Iaco and Maggio, Pagani and Panarello), Economy and finance (Biffingandi and Zeli, Giacalone) and Social media (Mariani and Marletta). As regards methodological approaches, the statistical methods and models used are Regression models (Davino et al., De Iaco and Maggio, Iannario and Monti, Pagani and Panarello), Data analysis models (Camminatiello et al., Cavicchia and Sarnacchiaro, D’Ambra et al.), Techniques for building panels from archives, Missing data disambiguation, Assessing ranking dissimilarity and inter-group heterogeneity, Improving forecasting accuracy, Simulated data (Biffignandi and Zeli, Davino et al., Giacalone, Mariani and Marletta, Vanacore and Pellegrino). The heterogeneity of application and statistical approaches underline the broad spectrum of data analyses and the richness of the methodologies.