Publication Date:
2016
abstract:
Measuring academic educational quality presents three major difficulties, typical
of all customer satisfaction and service quality studies: the use of subjective scales; the
ordinal nature of the data; and the multifold structure of satisfaction. In order to solve these
problems, principal component analysis (PCA) of compositional data is proposed in this
work. The core idea behind this methodology is to analyze by PCA the relative information
within the data rather than focusing on absolute scores. This approach is discussed in
comparison with a widely used Item Response Theory method (the Partial Credit Model) in
order to assess its merits, e.g. always identifying a coherent preference structure. Both
procedures were, thus, carried out on a real dataset collected with the 2013/14 ANVUR
questionnaire by L’Universita´ di Napoli-L’Orientale.
of all customer satisfaction and service quality studies: the use of subjective scales; the
ordinal nature of the data; and the multifold structure of satisfaction. In order to solve these
problems, principal component analysis (PCA) of compositional data is proposed in this
work. The core idea behind this methodology is to analyze by PCA the relative information
within the data rather than focusing on absolute scores. This approach is discussed in
comparison with a widely used Item Response Theory method (the Partial Credit Model) in
order to assess its merits, e.g. always identifying a coherent preference structure. Both
procedures were, thus, carried out on a real dataset collected with the 2013/14 ANVUR
questionnaire by L’Universita´ di Napoli-L’Orientale.
Iris type:
1.1 Articolo in rivista
Keywords:
Educational quality, Customer satisfaction, Compositional analysis, Logratios, Partial Credit Model
List of contributors:
Simonacci, Violetta; Gallo, Michele
Published in: