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Analysis of a dependency model can be furthered by assessing whether a model and/or the impact of regressors on dependent variables differ if heterogeneity is observed. In other words, it may be interesting to assess differences between a global model estimated for a whole group and models estimated for sub-groups identified on the basis of known categorical variables external to the model, as those variables may identify partitions characterized by dependency structure heterogeneity. This is particularly important in decision-making as policies based on the generic model could yield inaccurate and biased results. In this paper, we propose a procedure, the Pathmox approach that exploits the potential of segmentation trees to identify partitions in an initial set of data characterized by different linear regression patterns. We will apply this new approach to measure the visitors' satisfaction to attend a Spanish football match at the stadium. Thus, we will analyze the relationship between two significant aspects related to the visitors' satisfaction: stadium service quality and image of the football team, taking into account five visitors' background variables as potential sources of heterogeneity: age, gender, if they were tourist (yes or not), if it was the first time at the stadium (yes or not), and level of involvement with the football team. From a decision-making perspective, the paper contributes evidence exemplifying how an apparently representative global model can in fact mask different relationships between variables due to heterogeneous data, underlining the importance of accounting for heterogeneity when defining new policies.

Decision making.

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Statistical surveys represent an important source of scientific knowledge and a valid decision support tool in many fields, from social studies to economics, market research, health studies, and others. Scientists have tackled most of the methodological issues concerning surveys and the scientific literature offers excellent proposals for planning and conducting surveys. Nevertheless, surveys often require the achievement of aims that either deviate from the methodology or do not have a specific solution at all. This book focuses on survey theory and applications, providing insight and innovative solutions to face problems in data collection and integration, complex sample design, opinion questionnaire design, and statistical estimation. Formal rigour and simple language, together with real-life examples, will make the book suitable to both practitioners involved in applied research and to academics interested in scientific developments in the survey field.

Social sciences --Statistical methods --Computer programs --Handbooks, manuals, etc. --- Social Sciences --statistics & numerical data --Handbooks. --- Stata --Handbooks, manuals, etc. --- Statistics -- Handbooks. --- Sampling (Statistics) --- Surveys --- Social Sciences --- Mathematics --- Physical Sciences & Mathematics --- Statistics - General --- Mathematical Statistics --- Methodology --- Methodology. --- Random sampling --- Statistics of sampling --- Government surveys --- Statistics. --- Social sciences. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Methodology of the Social Sciences. --- Statistics for Social Science, Behavioral Science, Education, Public Policy, and Law. --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics --- Mathematical statistics --- Mathematical geography

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Environmental sustainability is one of the main goals of all countries in the world. Sustainable Development Goals (SDGs) have been proposed by the United Nations in 2015. The purpose of this paper is to explore if and how European countries achievethe goals of environmental sustainability (tracked by the SDGs number 13, 14, and 15). In particular, SDG 13 refers to climate change and its impacts; SDG 14 refers to the conservation of water and marine resources while the last one; SDG 15 deals with the preservation of forests. The reference methodology of the paper are the Self-Organizing Maps proposed by Kohonen in 1982 as an unsupervised clustering method in the framework of artificial neural networks. The proposed analysis considers the 23 indicators related to the three SDGs of environmental sustainability and aims to explore and identify groups of countries with similar characteristics through a dimensionality reduction. Such clusters will be visually represented in a two-dimensional map. The proposed analysis considers the most recent data for all the above SDGs, which is 2018, with the aim of classifying the countries in terms of environmental sustainability and highlighting possible implications for policymakers. An analysis of the network accuracy is shown, using appropriate indicators. These results allow us to see which countries have achieved these goals and how they have deviated from them.

Economic development. --- Economic policy. --- Sustainable development.

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The volume collects the papers presented at the Conference “Stat.Edu’21 -New Perspectives in Statistics Education”. The Conference was held at the Department of Political Sciences of the University of Naples Federico II (25-26 March 2021). The conference was the final event of the “ALEAS - Adaptive LEArning in Statistics”, an ERASMUS+ project (https://aleas-project.eu) developed in the period 2018-2021 to design and implement an Adaptive LEArning system able to offer personalised learning paths to students, with the purpose to provide them remedial advice to deal with the “statistics anxiety”.Stat.Edu’21 aimed at stimulating discussions, solicitations and contributions around the central theme of ALEAS, the development of adaptive learning systems in the field of Higher Education as a complementary tool for traditional courses and promote a community of practice in this field.The volume collects 12 papers reporting reflections and quantitative studies covering mainly three topics: the assessment of the effects of anxiety or more generally of a different attitude in the study of Statistics, tools and methods for the assessment of training paths and technology-based learning experiences Il volume raccoglie i contributi presentati alla conferenza “Stat.Edu’21 -New Perspectives in Statistics Education”. La Conferenza è stata ospitata dal Dipartimento di Scienze Politiche dell’Università degli Studi di Napoli Federico II (25-26 marzo 2021). La conferenza è stata organizzata come evento finale del progetto ERASMUS+ “ALEAS - Adaptive LEArning in Statistics” (https://aleas-project.eu) che si è svolto dal 2018 al 2021. Il progetto ha avuto l’obiettivo di sviluppare e implementare un sistema di apprendimento adattivo che offra percorsi di apprendimento personalizzati agli studenti, con lo scopo ultimo di aiutare gli studenti a fronteggiare l’ansia statistica.Stat.Edu’21 ha stimolato riflessioni, discussioni e contributi sul tema di ALEAS e sullo sviluppo di sistemi di apprendimento adattivo in ambito universitario come strumenti complementari ai corsi tradizionali e contribuito lo scambio di buone pratiche.Il volume comprende 12 contributi che propongono riflessioni e studi quantitativi in particolare su 3 temi: la valutazione degli effetti dell’ansia o più generalmente lo studio di diverse attitudini nello studio della statistica, strumenti e metodi per la valutazione dei percorsi di insegnamento e le esperienze di apprendimento basate sulla tecnologia.

technology-based platforms, adaptive learning systems, statistical methods, statistics anxiety, higher education

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The volume collects the papers presented at the Conference “Stat.Edu’21 -New Perspectives in Statistics Education”. The Conference was held at the Department of Political Sciences of the University of Naples Federico II (25-26 March 2021). The conference was the final event of the “ALEAS - Adaptive LEArning in Statistics”, an ERASMUS+ project (https://aleas-project.eu) developed in the period 2018-2021 to design and implement an Adaptive LEArning system able to offer personalised learning paths to students, with the purpose to provide them remedial advice to deal with the “statistics anxiety”.Stat.Edu’21 aimed at stimulating discussions, solicitations and contributions around the central theme of ALEAS, the development of adaptive learning systems in the field of Higher Education as a complementary tool for traditional courses and promote a community of practice in this field.The volume collects 12 papers reporting reflections and quantitative studies covering mainly three topics: the assessment of the effects of anxiety or more generally of a different attitude in the study of Statistics, tools and methods for the assessment of training paths and technology-based learning experiences Il volume raccoglie i contributi presentati alla conferenza “Stat.Edu’21 -New Perspectives in Statistics Education”. La Conferenza è stata ospitata dal Dipartimento di Scienze Politiche dell’Università degli Studi di Napoli Federico II (25-26 marzo 2021). La conferenza è stata organizzata come evento finale del progetto ERASMUS+ “ALEAS - Adaptive LEArning in Statistics” (https://aleas-project.eu) che si è svolto dal 2018 al 2021. Il progetto ha avuto l’obiettivo di sviluppare e implementare un sistema di apprendimento adattivo che offra percorsi di apprendimento personalizzati agli studenti, con lo scopo ultimo di aiutare gli studenti a fronteggiare l’ansia statistica.Stat.Edu’21 ha stimolato riflessioni, discussioni e contributi sul tema di ALEAS e sullo sviluppo di sistemi di apprendimento adattivo in ambito universitario come strumenti complementari ai corsi tradizionali e contribuito lo scambio di buone pratiche.Il volume comprende 12 contributi che propongono riflessioni e studi quantitativi in particolare su 3 temi: la valutazione degli effetti dell’ansia o più generalmente lo studio di diverse attitudini nello studio della statistica, strumenti e metodi per la valutazione dei percorsi di insegnamento e le esperienze di apprendimento basate sulla tecnologia.

Society & social sciences --- technology-based platforms, adaptive learning systems, statistical methods, statistics anxiety, higher education --- technology-based platforms, adaptive learning systems, statistical methods, statistics anxiety, higher education