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The Italian National Institute of Statistics (ISTAT) declined a multidimensional approach to measure equitable and sustainable well-being (Benessere Equo Sostenibile, BES) at a detailed territorial level, that is, at the provincial level (NUTS3) entailing a wide spectrum of indicators grouped into domains related to Health, Education, Work and life balance, Economic well-being, Social relationships, Politics and institutions, Security, Landscape and cultural heritage, Environment, Innovation research and creativity, and Quality of services. These indicators can help in describing the territories because they can spot situations of concern, such as in the South of Italy. The gap between North and South Italy has increased over time, a picture we can describe using each of the indicators, but also by jointly considering them by using a factor analysis and clustering and constructing a composite indicator. Contrary to some researchers who have suggested the employment of time series indicators for the period 2004–2016 looking for a latent factor, that takes into account the joint temporal trend of the indicators, in this paper, the situation among three different moments over time is compared: before the 2008 crisis and a few years after it. To do this, we have chosen a certain number of indicators on the basis of their features. [ABSTRACT FROM AUTHOR]

This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success in the university career. Although, great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level in particular in the Italian context. Indeed in the university context, the availability and possibility to use of support tools, that enable the student to achieve educational success, is still not sufficiently adequate. In this paper we used bivariate association tests and cluster analysis, in order to identify the most suitable compensatory tools and support strategies that can facilitate the students' performance in higher education. The data were obtained through the voluntary participation of Italian students, enrolled in a bachelor degree course, with certified diagnosis of dyslexia. Six groups of students were identified from the cluster analysis, defining specific support tools and learning strategies for each group. Furthermore, through the creation of these six groups, it was possible to describe "profiles" that highlight the risk factors (late diagnosis) and-or protection factors (such as associations, support from friends and family) in analyzing the academic career of students with dyslexia. Therefore, starting from these data, through artificial intelligence it will be possible to identify and suggest study methodologies and create specific support tools for each student that can enable her/him to achieve educational success in her/his academic career. [ABSTRACT FROM AUTHOR]

This paper contributes to the quantitative literature on social support networks for childcare in Italy. As italy is a country characterised by a familistic welfare model, women who lack kin-based support networks, which are indeed a useful tool in this type of countries, may be disadvantaged. We considered foreign working women that have at least one cohabiting child in preschool age, and, using large scale survey data, we defined and built their ECSEN (Effective Children Support Ego-Network). We synthesized the networks observed into five main typologies through a cluster analysis, followed by an aggregative inductive procedure. This allowed to single out the support given by kin members and by other children, and to highlight situations of potential social frailty. We then identified three situations of interest that we further analysed through logistic modeling. Results underline the crucial role of living arrangements and of kin's availability in shaping the ECSEN, highlight that, differently from other family behaviours, cultural background and country of origin play a marginal role, and identify some Italian specificities. [ABSTRACT FROM AUTHOR]

This paper contributes to the quantitative literature on social support networks for childcare in Italy. As italy is a country characterised by a familistic welfare model, women who lack kin-based support networks, which are indeed a useful tool in this type of countries, may be disadvantaged. We considered foreign working women that have at least one cohabiting child in preschool age, and, using large scale survey data, we defined and built their ECSEN (Effective Children Support Ego-Network). We synthesized the networks observed into five main typologies through a cluster analysis, followed by an aggregative inductive procedure. This allowed to single out the support given by kin members and by other children, and to highlight situations of potential social frailty. We then identified three situations of interest that we further analysed through logistic modeling. Results underline the crucial role of living arrangements and of kin's availability in shaping the ECSEN, highlight that, differently from other family behaviours, cultural background and country of origin play a marginal role, and identify some Italian specificities. [ABSTRACT FROM AUTHOR]

One of the most controversial steps in Composite Indicators (CIs) construction is the selection of one (possibly) best weighting technique. In this paper, we introduce a new endogenous weighting methodology developed in an extended Item Response Theory (IRT) framework. As weighting is much more thorough when carried out by accounting for the dimensionality of a dataset rather than by ignoring it, we suggest to assign weights on the basis of the discrimination parameters estimated through a multidimensional twoparameter logistic IRT model. Specifically, the procedure is developed through two consecutive steps. The first applies a hierarchical clustering algorithm to ascertain the number of dimensions measured by the data. The second estimates the discrimination parameters under the multidimensional two-parameter logistic model selected at the first step. The discrimination parameters can then be used to compare and weight the items that refer to the same dimension. Besides, in order to make such discrimination indices comparable across dimensions, the distribution of the latent trait is standardised for each dimension. The potentialities of this novel weighting technique are illustrated through an application to educational data, which refer to a national standardised test developed and collected by the Italian National Institute for the Evaluation of the Education System. [ABSTRACT FROM AUTHOR]