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. Raffinetti
Primo
;
P.A. Ferrari
Ultimo
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.
Dependence relationship; Monte carlo simulations; Ordinal data
Settore SECS-S/01 - Statistica
2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/347526
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