TWO METHODS FOR COMPOSITE INDICATOR DIMENSION REDUCTION BASED ON RANK CORRELATION

Composite indicators are very useful in addressing complex variables that cannot be directly observed: they can be used to assess for example quality of life and customer satisfaction. In practice, it is very often of interest to reduce the dimension of a composite indicator by selecting among its components the most important ones. In this paper we propose a method for reducing the dimension of composite indicators based on the comparison of rank correlation coefficients and we compare it with another method. A practical application to university student satisfaction data is presented. Moreover, we evaluate how the choice of the rank correlation coefficient influences the results of the practical application.

TWO METHODS FOR COMPOSITE INDICATOR DIMENSION REDUCTION BASED ON RANK CORRELATION / Marozzi, M; Cozzucoli, Paolo Carmelo. - In: STATISTICA & APPLICAZIONI. - ISSN 1824-6672. - XII:1(2014), pp. 39-46.



Titolo: TWO METHODS FOR COMPOSITE INDICATOR DIMENSION REDUCTION BASED ON RANK CORRELATION
Autori: 
COZZUCOLI, Paolo Carmelo
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Data di pubblicazione: 2014
Rivista: 
STATISTICA & APPLICAZIONI  
Citazione: TWO METHODS FOR COMPOSITE INDICATOR DIMENSION REDUCTION BASED ON RANK CORRELATION / Marozzi, M; Cozzucoli, Paolo Carmelo. - In: STATISTICA & APPLICAZIONI. - ISSN 1824-6672. - XII:1(2014), pp. 39-46.
Handle: http://hdl.handle.net/20.500.11770/140900
Appare nelle tipologie:1.1 Articolo in rivista

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http://hdl.handle.net/20.500.11770/140900
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