Stefania Capecchi

A multi-product approach for detecting subjects’ and objects’ covariates in consumer preferences

– A different framework based on a parametric version of the process generating the hedonic scores is adopted. More precisely, a probability distribution for ordinal responses is proposed as a mixture of two components, denoted as feeling (as expressed preference) and uncertainty component (as inherent indecision). The purpose of this paper is to analyse the effect of covariates on the consumers’ behaviour pattern according to a statistical model. , – Sample data come from a multidisciplinary research aimed to improve the quality and marketability of soft fruits. Then, a stochastic model with subjects’ and objects’ covariates is built and the interpretation of significant results is discussed. , – The joint effects of personal characteristics and chemical contents of juice on the hedonic scores given by consumers are examined and graphically depicted by means of a significant model. , – The paper suggests a multi-product approach to expressed hedonic scores by means of a generalization of CUB models.

Modelling the Latent Components of Personal Happiness

We discuss a class of statistical models able to measure the self-evaluation of happiness by means of a sample of respondents and investigate the ability of this proposal to enhance the different contribution of subjective, environmental and economic variables. The approach is based on a mixture model introduced for interpreting the ordered level of happiness as a combination of a real belief and a surrounding uncertainty: these unobserved components may be easily parameterized and immediately related to subjects’ covariates. An empirical evidence is supported on data set derived by the Survey of Household Income and Wealth (SHIW) conducted by the Bank of Italy.

Modelling Job Satisfaction of Italian Graduates

Different models have been implemented to observe worker conditions, abilities, leadership, decision-making attitudes and other related concerns. This paper aims to investigate the job satisfaction of a large sample of Italian graduates with a model-based approach derived by a mixture distribution. Sample data have been collected in the 2010 AlmaLaurea survey on graduates employment conditions, 5 years after their degree. We highlight several issues which are effective in assessing the performance of the academic system and detecting graduates’ responses towards labour market using cub models approach. A specific contribution of this paper consists in emphasizing the possibility to achieve immediate interpretation and visualization of the main relationships between responses concerning job satisfaction and characteristics of the interviewees.

Measuring Indecision in Happiness Studies

The main objective of this paper is to evaluate the degree of uncertainty in self-reported happiness responses by means of a statistical model able to detect the relevant features of the expressed ratings. We consider a mixture model to address a twofold research question: how can we measure the indecision in expressed well-being; how to assess if this latent trait varies depending on the covariates of those surveyed? The selected modelling approach investigates the feeling/agreement component, making the underlying indecision explicit without imposing extra constraints to the model. Furthermore, our proposal allows to enhance the presence of a “refuge” option in the response patterns. The effects of individual characteristics may be highlighted, when significant. Results are presented stemming from an observational study showing that responses are characterized by a large variability among subjects. The methodology here experimented may be considered a general one since it can be exploited both in observational and in experimental surveys.

An Inflated Model to Account for Large Heterogeneity in Ordinal Data

In sample surveys where people are asked to express their opinions, a high level of indecision among respondents may generate sub-optimal statistical analyses caused by large heterogeneity in the responses. We discuss a model belonging to the class of generalized cub models that is suitable for this kind of surveys. Then, we examine a real case study where the observed heterogeneity as well as respondents’ indecision can be analyzed within the theoretical framework of the proposed model leading to convincing interpretations. A comparison with current literature and some concluding remarks end the paper.