Assessing inter-rater reliability with heterogeneous variance components models: Flexible approach accounting for contextual variables

TL;DR

This paper introduces a Bayesian modeling approach to assess inter-rater reliability, accounting for heterogeneity caused by covariates, and demonstrates its effectiveness through simulations and real data examples.

Contribution

It proposes a flexible Bayesian method for modeling heterogeneity in IRR due to covariates, including model selection and averaging techniques.

Findings

Bayesian model-averaging provides robust IRR estimates.

The method outperforms some existing approaches in simulations.

Real data examples show practical usefulness and flexibility.

Abstract

Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for implementation of policies with the potential to decrease measurement error and to increase IRR by focusing on the most relevant subgroups. In this study, we propose a flexible approach for assessing IRR in cases of heterogeneity due to covariates by directly modeling differences in variance components. We use Bayes factors to select the best performing model, and we suggest using Bayesian model-averaging as an alternative approach for obtaining IRR and variance component estimates, allowing us to account for model uncertainty. We use inclusion Bayes factors considering the whole model space to provide evidence for or against differences in variance components due to covariates. The proposed method is compared with other Bayesian and frequentist approaches in a simulation study, and we demonstrate its superiority in some situations. Finally, we provide real data examples from grant proposal peer-review, demonstrating the usefulness of this method and its flexibility in the generalization of more complex designs.