Main Article Content
Education outputs are detected through a large number of statistical tools suitable to support policy design of governing authorities and decision making process of stakeholders. Several proposals have emerged to outline the aspects that mostly shape the composite concepts of both courses evaluation and internal/external effectiveness, considered as multifaceted latent constructs, such as graduates well-being at work. This paper aims at studying job satisfaction of a large sample of Italian graduates (Master of Arts) through an innovative framework for ordinal data modelling, which allows to take into account both feeling/satisfaction and heterogeneity/uncertainty in response patterns. The effects of significant subjective and structural covariates on self-declared assessments are considered as prominent job satisfaction drivers and a comprehensive model which includes past evaluations is examined. Data stem from the AlmaLaurea XVII Survey on Italian graduates’ working conditions carried out in 2014.