Dyadic data analysis (DDA) is increasingly being used to better under- stand, analyze and model intra- and inter-personal mechanisms in various types of dyads such as husband-wife, caregiver-patient, doctor-patient, parent-child or athlete-coach as in our example. A key strength of the DDA is its flexibility to take the (non)independence available in the dyads into account. In this article, we illustrate the value of using DDA to examine how sports performance is perceived by an athlete and if it is consistent with the declared performance by his/her coach. A probability summary for ordered comparison of groups referred to a measure of stochastic superiority is used to indicate the consistency of perceived assessments.
Ordinal probability effect measures for dyadic analysis in cumulative models / Iannario, M.; Vistocco, D.. - (2020), pp. 1194-1199. (Intervento presentato al convegno 50th Scientific Meeting of the Italian Statistical Society).
Ordinal probability effect measures for dyadic analysis in cumulative models
Iannario M.;Vistocco D.
2020
Abstract
Dyadic data analysis (DDA) is increasingly being used to better under- stand, analyze and model intra- and inter-personal mechanisms in various types of dyads such as husband-wife, caregiver-patient, doctor-patient, parent-child or athlete-coach as in our example. A key strength of the DDA is its flexibility to take the (non)independence available in the dyads into account. In this article, we illustrate the value of using DDA to examine how sports performance is perceived by an athlete and if it is consistent with the declared performance by his/her coach. A probability summary for ordered comparison of groups referred to a measure of stochastic superiority is used to indicate the consistency of perceived assessments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
