he great interest in quantitative social research has led to the development of specific statistical techniques suitable in dealing with dependence between variables also in the presence of ordinal data. A specific index, hereafter called monotonic dependence coefficient (MDC), was provided as a monotonic dependence measure. Due to its properties and specific features, MDC overcomes the Pearson's correlation coefficient, since it captures not only linear dependence relationships but also any general monotonic one. The MDC adequacy is validated by a simulation study assessing its performance with respect to the traditional Pearson's correlation coefficient. Finally, a real application of MDC to real data is also illustrated.
New perspectives for the MDC index in social research fields / E. Raffinetti, P.A. Ferrari (STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION). - In: Advances in Statistical Models for Data Analysis / [a cura di] I. Morlini, T. Minerva, M.Vichi. - Prima edizione. - [s.l] : Springer, 2015. - ISBN 9783319173764. - pp. 211-219 (( Intervento presentato al 9. convegno CLADAG 2013 tenutosi a Modena nel 2013 [10.1007/978-3-319-17377-1_22].
New perspectives for the MDC index in social research fields
E. RaffinettiPrimo
;P.A. FerrariUltimo
2015
Abstract
he great interest in quantitative social research has led to the development of specific statistical techniques suitable in dealing with dependence between variables also in the presence of ordinal data. A specific index, hereafter called monotonic dependence coefficient (MDC), was provided as a monotonic dependence measure. Due to its properties and specific features, MDC overcomes the Pearson's correlation coefficient, since it captures not only linear dependence relationships but also any general monotonic one. The MDC adequacy is validated by a simulation study assessing its performance with respect to the traditional Pearson's correlation coefficient. Finally, a real application of MDC to real data is also illustrated.File | Dimensione | Formato | |
---|---|---|---|
Advances_in Statistical_Models_for_Data_Analysis_Series_Studies_in Classification_Data_Analysis_and_Knowledge_Organization_2015.pdf
accesso riservato
Tipologia:
Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione
125.71 kB
Formato
Adobe PDF
|
125.71 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.