Evaluation can be described as the psychological process which a subject has to perform when a subject is requested to give a determination of merit regarding an item (the attributes of a service, a product or in general, any tangible or intangible object) using a certain ordinal scale. In this article a simplified description of the evaluation process is presented in order to specify the final result originated from such a process as the combined effect of two unobservable components, one related to the individual feeling for the object under evaluation and the other related to the intrinsic uncertainty which affects any human decision. Later, a class of models (named CUB ) is logically derived from these assumptions, and properties and extensions are illustrated. Finally, inferential issues and numerical procedures for maximum likelihood parameter estimation and related asymptotic inference are discussed; in addition, the main steps of the EM estimation algorithm is provided for a specific CUB model. The article, in the last section, deals with possible applications of this class of models for ordinal data analysis. In particular, a data set concerning students’ satisfaction with university ”orientation” services is examined.

A class of statistical models for evaluating services and performances

CORDUAS, MARCELLA;IANNARIO, MARIA;PICCOLO, DOMENICO
2009

Abstract

Evaluation can be described as the psychological process which a subject has to perform when a subject is requested to give a determination of merit regarding an item (the attributes of a service, a product or in general, any tangible or intangible object) using a certain ordinal scale. In this article a simplified description of the evaluation process is presented in order to specify the final result originated from such a process as the combined effect of two unobservable components, one related to the individual feeling for the object under evaluation and the other related to the intrinsic uncertainty which affects any human decision. Later, a class of models (named CUB ) is logically derived from these assumptions, and properties and extensions are illustrated. Finally, inferential issues and numerical procedures for maximum likelihood parameter estimation and related asymptotic inference are discussed; in addition, the main steps of the EM estimation algorithm is provided for a specific CUB model. The article, in the last section, deals with possible applications of this class of models for ordinal data analysis. In particular, a data set concerning students’ satisfaction with university ”orientation” services is examined.
File in questo prodotto:
File Dimensione Formato  
CORDUASIANNAPICCOLO.pdf

non disponibili

Tipologia: Documento in Post-print
Licenza: Accesso privato/ristretto
Dimensione 1.13 MB
Formato Adobe PDF
1.13 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
CORDUASIANNAPICCOLO.pdf

non disponibili

Tipologia: Documento in Post-print
Licenza: Accesso privato/ristretto
Dimensione 1.13 MB
Formato Adobe PDF
1.13 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/353652
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 31
social impact