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Educational achievement can be considered a multifaceted issue, which comprises different domains of learning. In Italy, on one hand the INVALSI tests administered to students through the schooling years aim to measure the ability of students in numeracy, literacy and English reading and listening competencies, separately. On the other hand, the high school final mark may be considered an overall performance outcome, formed by the combination of several marks in different subjects. Finally, at university academic achievement may be represented by the number of credits earned during the first year of enrolment, usually considered a good predictor of successful academic performances. The aim of the present work is to understand if the INVALSI scores, the high school final mark and the number of credits earned in the first academic year are associated. More specifically, our objective is twofold: first, we intend to verify if and how the INVALSI scores are associated with students’ high school final mark; second, we aim to check if the INVALSI scores and / or the high school final mark are predictive of students’ career in terms of credits earned in the first year. We will interpret our results concentrating our attention on eventual differences depending on type of school, university, field of study, student’s geographical area of residence. We use the MOBYSU.it database, selecting nearly 200.000 students who obtained their high school diploma in Italy in 2019 and enrolled in an Italian university in academic year 2019/2020. For the first objective, we estimate a multilevel ordered logit models, with students nested within high schools; as for the second objective, we estimate a cross-classified multilevel models, with students nested within high schools and athenaeums. We interpret our results in the light of assessing eventual divergences in students’ performances during the transition from high school to university.

Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to measure and detect the possession of abilities underlying the above-mentioned soft skills. This paper aims to address both aspects of this topic by presenting the results of a research we conducted in the period October – December 2020 on students enrolled in various degree courses at the University of Florence. To the best of our knowledge, to date this is the largest available database on the subject in the Italian University System. It has been obtained by a three-stage initiative. We started from an “entrance” examination for assessing the students' initial abilities. This test comprised ten questions, each of which was centered on a specific reasoning construct. The results we have collected show that there is a widespread lack of understanding of basic patterns that are common in the everyday way of arguing. Students then underwent a short training course, using formal logic techniques in order to strengthen their abilities, and afterwards took an “exit” examination, replicating the structure and the questions difficulty of the entrance one in order to evaluate the effectiveness of the course. Results show that the training was beneficial.

Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to detect and measure the possession of abilities underlying the aforementioned skills. This paper addresses both aspects of this topic by presenting the results of a research that we conducted between October and December 2020 on students enrolled in various degree courses at the University of Florence. The dataset has been collected by a three-stage initiative. We started from an entrance examination to assess the students' initial abilities. This test included ten questions, each regarding a specific aspect of logical reasoning. Then, students underwent a short training course to strengthen their knowledge of logic. At the end of the short course, students underwent a final examination to evaluate the effectiveness of the training. To verify the effectiveness of the training program we used students’ characteristics into a Structural Equation Model comparing the distribution of abilities before and after the training.

Nowadays, the information transfer speed (on the web, but not only) requires the predisposition of ever more adequate analysis tools for working with data and increasingly faster algorithms, in order to allow the so-called decision maker to make decision based on information which can become obsolete very quickly with time. In the current situation, analysing this information in order to simulate complex decision-making scenarios could prove fundamental to secure an advantage over competitors. This text introduces the true art of Statistical Computing. In other words, it illustrates how computer programming skills in the development of algorithms can be used within Statistics for the virtual simulation and replication of realities and experiments of varying complexity. In order to do so, it uses the excellent development environment “R”.

This book includes 25 peer-reviewed short papers submitted to the Scientific Opening Conference titled “Statistics and Information Systems for Policy Evaluation”, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Department of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring.