A Regulatory Impact Analysis (RIA) approach based on evolutionary association patterns

The present paper focuses on ex post analysis to asses the impact of an adopted policy by measuring system performance. Since accurate impact assessment requires in-depth knowledge of the structure underlying the system, this contribution proposes a suitable use of multidimensional data analysis (MDA) to investigate the associations characterizing the indicators/attributes of the system. The general aim is to identify homogeneous subsets of objects that are described by subsets of attributes. This approach was planned to study students performance in Italian universities: the focus is on student careers. The example data set is a data mart selected from the University of Macerata data base and refers to the students at the Economics Faculty from 2001 to 2007.