We present a Bayesian approach for the analysis of rating data when a scaling component is taken into account. Model-based probability effect measures for comparing distributions of several groups, adjusted for explanatory variables affecting both location and scale components, are computed. Markov Chain Monte Carlo techniques are implemented to obtain parameter estimates and the mentioned measures. An analysis on students’ evaluation of a university orientation service is carried out to assess the performance of the method and make more valuable the decision making process of university players (stakeholders).
Modelling scale effects via a Bayesian approach: an application to decision making in public sector / Iannario, Maria; Tarantola, Claudia. - (2022), pp. 243-248. (Intervento presentato al convegno IES2022-Innovation & Society 5.0: Statistical and Economic Methodologies for Quality Assessment).
Modelling scale effects via a Bayesian approach: an application to decision making in public sector
Maria Iannario
;
2022
Abstract
We present a Bayesian approach for the analysis of rating data when a scaling component is taken into account. Model-based probability effect measures for comparing distributions of several groups, adjusted for explanatory variables affecting both location and scale components, are computed. Markov Chain Monte Carlo techniques are implemented to obtain parameter estimates and the mentioned measures. An analysis on students’ evaluation of a university orientation service is carried out to assess the performance of the method and make more valuable the decision making process of university players (stakeholders).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
