A composite indicator via hierarchical disjoint factor analysis for measuring the Italian football teams’ performances

In the last years, with the data revolution and the use of new technologies, phenomena are frequently described by a huge quantity of information useful for making strategical decisions. In the current ”big data” era, the interest of statistics into sports is increasing over the years, sportive and economic data are collected for all teams which use statistical analysis in order to improve their performances. For dealing with all this amount of information, an appropriate statistical analysis is needed. A priority is having statistical tools useful to synthesise the information arised from the data. Such tools are represented by composite indicators, that is, non-observable latent variables and linear combination of observed variables. The strategy of construction of a composite indicator used in this paper is based on a non-negative disjoint and hierarchical model for a set of quantitative variables. This is a factor model with a hierarchical struc- ture formed by factors associated to subsets of manifest variables with positive loadings. In this paper, a composite indicator for measuring the Italian football teams’ performances, in terms of sportive and economic variables, is proposed

An ultrametric model for building a composite indicator system to study climate change in European countries

Il cambiamento climatico e uno dei temi più urgenti nel dibattito pubblico, incluso nell’Agenda 2030 per lo sviluppo sostenibile adottata dalle Nazioni Unite, a causa dei rischi che può comportare per la vita umana. Esso rappresenta un fenomeno multidimensionale definito da diverse dimensioni, ognuna delle quali misurata da un insieme di variabili direttamente osservabili, che riguardano le emissioni di gas serra, le cause del cambiamento climatico, le sue conseguenze per gli umani e la natura e gli sforzi messi in campo dal genere umano per evitare e adattarsi a tali conseguenze. In questo lavoro, introduciamo un nuovo approccio basato su modello per studiare questo fenomeno e le sue diverse caratterizzazioni nei paesi europei. La proposta metodologica ha l’obiettivo di costruire un sistema di indicatori compositi per identificare i principali fattori che determinano il cambiamento climatico e fornire di conseguenza indicazioni per attuare politiche nel contesto europeo volte a combattere il cambiamento climatico.Nowadays climate change is one of the most urgent topics in public de bate, also included into the 2030 Agenda for Sustainable Development adopted by the United Nations, because of its risks for human life. It represents a multidimensional phenomenon defined by different dimensions that pertain to greenhouse as emissions, human causes of climate change, impacts on humans and natural systems, and efforts of human to avoid and adapt to the consequences, each of which is described by a set of variables directly observed. In this paper, we introduce a new model-based approach to study this phenomenon and its different characterization in European countries. The proposal aims at building a system of composite indicators in order to understand the main determinants of this phenomenon, and to provide guidelines for policy decisions to combat climate change in the European Union framework

A composite indicator for the waste management in the EU via Hierarchical Disjoint Non-Negative Factor Analysis

In the last years, the quantity of information and statistics about waste management are more and more consistent but so far, few studies are available in this field. The goal of this paper is of producing a model-based Composite Indicator of "good" Waste Management, in order to provide a useful tool of support for EU countries' policy-makers and institutions. Composite Indicators (CIs), usually, are multidimensional concepts with a hierarchical structure characterized by the presence of a set of specific dimensions, each one corresponding to a subsets of manifest variables. Thus, we propose a CI for Waste Management in Europe by using a hierarchical model-based approach with positive loadings. This approach guarantees to comply with all the good properties on which a composite indicator should be based and to detect the main dimensions (i.e., aspects) of the Waste Management phenomenon. In other terms, this paper provides a hierarchically aggregated index that best describes the Waste Management in EU with its main features by identifying the most important high order (i.e., hierarchical) relationships among subsets of manifest variables. All the parameters are estimated according to the maximum likelihood estimation method (MLE) in order to make inference on the parameters and on the validity of the model

The consumption of genetically modified foods in Italian high school students

Genetically modified organisms (GMOs) can be defined as organisms in which the DNA has been altered in a way that does not occur naturally. Such methods are used to create GM plants – which are then used to grow (GM) food crops. GM foods have the potential to solve many of the world’s hunger and malnutrition problems, and to help protect and preserve the environment by increasing yield and reducing reliance upon chemical pesticides and herbicides. Nevertheless, the consumption of GM foods provokes doubts and hesitations among consumers, especially in Italy. This paper has two aims, the first is to investigate genetically modified (GM) foods consumption in Italian high school students through a large sample size survey on 2122 students randomly selected in 39 schools of a metropolitan area (Naples, South-Italy). The second, by examining the behavioural process that drives individual’s perceptions of GM food taking advantage of an empirical choice methodology that corrects for endogeneity in decision making relationships, namely structural equation model (SEM). The results show that a very large percentage of students never or rarely eat GM food and a lot of them do not suggest the consumption of GM food. The proposed SEM is a full formative measurement model and shows that GM foods consumption in Italian students depends on the knowledge of GMO and on the impact of the GMO on the men’s health and on the environment. Therefore, in order to orient population it could be realized a standardized evaluation systems relative to human health and environment consequences produced by GM organisms and GM foods

On the choice of weights in aggregate compositional data analysis

In this paper, we distinguish between two kinds of compositional data sets: elementary and aggregate. This fact will help us to decide the choice of the weights to use in log interaction analysis of aggregate compositional vectors. We show that in the aggregate case, the underlying given data form a paired data sets composed of responses and qualitative covariates; this fact helps us to propose two approaches for analysis-visualization of data named log interaction of aggregates and aggregate of log interactions. Furthermore, we also show the first-order approximation of log interaction of a cell for different choices of the row and column weights.Comment: 3 figures, 1 table, 17 page

An ultrametric model to build a Composite Indicators system

Negli ultimi anni l’utilizzo di indicatori compositi `e costantemente cresciuto, e la necessità di costruire degli indicatori compositi model-based con un forte approccio statistico `e sempre più importante per motivi di fiducia. In questo articolo proponiamo di costruire un sistema di indicatori compositi che possa misurare diversi livelli di relazioni tra (gruppi di) variabili seguendo una forma ultrametrica che individui una gerarchia sulle (gruppi di) variabili. Al fine di mostrare il suo potenziale e la sua applicabilità, la metodologia `e applicata per analizzare un dataset che contiene variabili riguardo la raccolta differenziata in Italia considerando sia le sue prestazioni che i suoi costi.In the last years, the use of composite indicators has consistently increased, and the necessity to build model-based composite indicators with a strong methodological statistical approach becomes more and more important for reasons of trustworthiness. In this paper, we propose to build a composite indicators system able to measure different levels of relations among (group of) variables according to an ultrametric form which detects a hierarchical structure upon (group of) variables. Each dimension is measured as a specific composite indicator which reflects a subset of variables. In order to show its potential and applicability, the methodology is employed to analyze a dataset which contains variables about separated waste collection in Italy taking into consideration both its performance and its costs

Decomposition of the Gray-Williams "tau" in main and interaction effects by ANOVA in three-way contingency table

The identification of meaningful relationships between two or more categorical variables is an important, and ongoing, element to the analysis of contingency tables. It involves detecting categories that are similar and/or different to other categories. Correspondence analysis can be used to detect such relationships by providing a graphical interpretation of the association between the variables, and it is especially useful when it is known that this association is of a symmetric nature. (Greenacre 1984), (Lebart et al. 1984). In this paper, we will explore the Gray-Williams index when used as the measure of association in non-symmetrical correspondence analysis (NSCA). It will be shown that, by concatenating a predictor variable of a three-way contingency table, the two measures are equivalent. The paper will analyse the sum of squares for nominal data partitioning the Sum of squares for main effects and the interaction in the sense of analysis of variance giving an orthogonal decomposition of Gray Williams index

The study of relationship between financial performance and points achieved by Italian football championship clubs via GEE and diagnostic measures

Football is undoubtedly the most powerful and most popular sport in Italy, linking communities and stirring emotions. The main goal of any Football Championship club is to achieve sport results. The study of the relationship between sport and economic results attracts the interest of many scholars belonging to different disciplines. Very informative is considered the connection, over short or long periods of time, between the points in the championship and the resource allocation strategies. The aim of this paper is to give a interpretation of this last link using the Generalized Estimating Equation (GEE) for longitudinal data. Some diagnostic measures and graphical plots for checking the adequacy of GEE method will be presented and used

A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction

Teachers’ performances also depend on whether and how they are satisfied with their job. Therefore, Teacher Job Satisfaction must be considered as the driver of teachers’ accomplishments. To plan future policies and improve the overall teaching process, it is crucial to understand which factors mostly contribute to Teacher Job Satisfaction. A Common Assessment Framework and Education questionnaire was administered to 163 Italian public secondary school teachers to collect data, and a second-order factor analysis was used to detect which factors impact on Teacher Job Satisfaction, and towhat extent. This model-based approach guarantees to detect factors which respect important properties: unidimensionality and reliability. All the coefficients are estimated according to the maximum likelihood estimation method in order tomake inference on the parameters and on the validity of themodel. Moreover, a new multi-group test