Autori: Spoto, Andrea, Calcagnì, Antonio, Mignemi, Giuseppe, Manolopoulou, Ioanna
Titolo: Mixture polarization in inter-rater agreement analysis: a Bayesian nonparametric index
Periodico: Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2024 - Volume: 33 - Fascicolo: 1 - Pagina iniziale: 325 - Pagina finale: 355

In several observational contexts where different raters evaluate a set of items, it is common to assume that all raters draw their scores from the same underlying distribution. However, a plenty of scientific works have evidenced the relevance of individual variability in different type of rating tasks. To address this issue the intra-class correlation coefficient (ICC) has been used as a measure of variability among raters within the Hierarchical Linear Models approach. A common distributional assumption in this setting is to specify hierarchical effects as independent and identically distributed from a normal with the mean parameter fixed to zero and unknown variance. The present work aims to overcome this strong assumption in the inter-rater agreement estimation by placing a Dirichlet Process Mixture over the hierarchical effects’ prior distribution. A new nonparametric index is proposed to quantify raters polarization in presence of group heterogeneity. The model is applied on a set of simulated experiments and real world data. Possible future directions are discussed.




SICI: 1618-2510(2024)33:1<325:MPIIAA>2.0.ZU;2-E

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