Binomial Mixture Modeling of University Credits

The paper reviews finite mixture models for binomial counts with concomitant variables. These models are well known in theory, but they are rarely applied. We use a binomial finite mixture to model the number of credits gained by freshmen during the first year at the School of Economics of the University of Florence. The finite mixture approach allows us to appropriately account for the large number of zeroes and the multimodality of the observed distribution. Moreover, we rely on a concomitant variable specification to investigate the role of student background characteristics and of a compulsory pre-enrollment test in predicting gained credits. In the paper, we deal with model selection, including the choice of the number of components, and we devise numerical and graphical summaries of the model results in order to exploit the information content of the concomitant variable specification. The main finding is that the introduction of the pre-enrollment test gives additional information for student tutoring, even if the predictive power is modest.     

A Hierarchical Ordinal Probit Model for the Analysis of Life Satisfaction in Italy

The aim of this paper is to study the individual and contextual determinants of life satisfaction in Italy. A hierarchical probit model is proposed for studying data with group structure and an ordinal response variable. The group structure is defined by the presence of micro observations embedded with regional contexts (macro observations), and the specification is at both of these levels. The methodology is applied to Eurobarometer survey data, with individuals viewed as micro observations and regions as macro observations. We find that Italy is not an homogeneous country with respect to life satisfaction, and that the regional environment contributes with the observed and unobserved characteristics of the individuals to life satisfaction level.