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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.

This volume collects the contributions presented at the conference “Data-driven Decision Making” organized by the Italian Association for Applied Statistics, held in Genoa from 12 to 14 September 2022. The papers cover a broad range of topics, with a common thread: the use of statistical methods to support decision-making both in public administrations and in private companies.

Structural equation modeling (SEM), especially partial least squares path modeling (PLS-PM) has become a mainstream method in many fields of research. In the last years it has been increasingly disseminated in a variety of disciplines. The researchers have been promoting this new statistical methods for the evaluation of policies. Generally, policy evaluation applies evaluation principles and methods to examine the content, implementation or impact of a policy. To better understand and characterize this trend, a bibliometric study of international papers on this subject has been developed in order to describe the use of SEM and PLS-PM approaches in the policy evaluation in the almost last 20 years. A total of 450 articles from 2000 to 2020 have been selected and analyzed in order to discover the research trends in this field and the main dimensions and words related to the terms “decision making” and “SEM-PLS” approach, that are most commonly employed in the scientific literature. The research has been conducted in theWeb of Science from ISI Web of Knowledge database and Scopus database, with the aim of identifying the major themes, authors, areas, types of the sources, titles, years of publication and countries of these publications, as well as the main themes related to the two topic analyzed

This book includes 40 peer-reviewed short papers submitted to the Scientific 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 Dept. 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.