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

TL;DR

This paper introduces a Bayesian nonparametric index to measure rater polarization, addressing individual variability in inter-rater agreement analysis by using a Dirichlet Process Mixture model, improving over traditional assumptions.

Contribution

It proposes a novel nonparametric index and Bayesian model to better capture heterogeneity and polarization among raters in agreement studies.

Findings

Model effectively captures rater heterogeneity.

Index $$ quantifies polarization in simulated and real data.

Outperforms traditional ICC in heterogeneous settings.

Abstract

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 $\lambda$ 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.