[Forum SIS] Avviso di seminario Paola Zuccolotto presso Dipartimento di Scienze Economiche, Università di Verona

[Luigi Grossi]

Avviso di seminario

Università di Verona - Dipartimento di Scienze Economiche
Via dell'artigliere, 19
Verona

Mercoledì 25 febbraio 2015 alle ore 12.30 - Aula C, Palazzo di Economia
 "Modeling rating data with Nonlinear CUB models"
Relatore: Paola Zuccolotto - Università di Brescia

Riassunto

The analysis of human perceptions is often carried out by resorting to questionnaires, where respondents are asked to express ratings about the objects being evaluated. The goal of the statistical tools proposed for this kind of data is to explicitly characterize the respondents' perceptions about a latent trait, by taking into account, at the same time, the ordinal categorical scale of measurement of the involved statistical variables. The seminar deals with a statistical model for rating data, obtained starting from a particular assumption about the unconscious mechanism driving individuals' responses on a rating scale. The basic idea derives from the founding paradigm of CUB models (Piccolo, 2003; D'Elia and Piccolo, 2005; Iannario and Piccolo, 2012). The described model is called Nonlinear CUB (Manisera and Zuccolotto, 2014). The main innovation of Nonlinear CUB models is that, in their framework, it is possible to define the new concept of transition probability, i.e. the probability of increasing one rating point at a given step of the decision process. Transition probabilities and the related transition plots are able to describe the state of mind of the respondents about the response scale used to express judgments. In particular, NLCUB can address the (possible) unequal spacing of the rating categories, that occurs when respondents, in their unconscious search for the ``right'' response category, find it easier to move, for example, from rating 1 to 2 than from rating 4 to 5. This corresponds to the concept of ``nonlinearity'' introduced by NLCUB models, defined as the nonconstantness of the transition probabilities. Case studies on real data are presented, in order to show the functioning of the model. R functions are available for computations. Research partially funded by STAR project (University of Naples Federico II) and partially by a grant from the European Union Seventh Framework Programme (SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions - SYRTO).



Cordiali saluti,
Luigi Grossi
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