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In several observational contexts where different raters evaluate a set ofitems, it is common to assume that all raters draw their scores from the sameunderlying distribution. However, a plenty of scientific works have evidencedthe relevance of individual variability in different type of rating tasks. Toaddress this issue the intra-class correlation coefficient (ICC) has been usedas a measure of variability among raters within the Hierarchical Linear Modelsapproach. A common distributional assumption in this setting is to specifyhierarchical effects as independent and identically distributed from a normalwith the mean parameter fixed to zero and unknown variance. The present workaims to overcome this strong assumption in the inter-rater agreement estimationby placing a Dirichlet Process Mixture over the hierarchical effects' priordistribution. A new nonparametric index $\lambda$ is proposed to quantifyraters polarization in presence of group heterogeneity. The model is applied ona set of simulated experiments and real world data. Possible future directionsare discussed.

Mixture polarization in inter-rater agreement analysis: a Bayesian nonparametric index