# Registered report protocol: Factors associated with inter-rater agreement in grant peer review

## TL;DR

This study explores factors influencing agreement among grant reviewers to improve the fairness and reliability of research funding decisions.

## Contribution

The study introduces a cross-classified regression model to analyze inter-rater agreement in grant reviews across multiple funders and disciplines.

## Key findings

- Inter-rater agreement may be influenced by reviewer similarity in gender and age.
- Application characteristics like funding amount and research area may affect agreement levels.
- Reviewer experience and expertise could correlate with higher agreement.

## Abstract

Grant peer review processes are pivotal in allocating substantial research funding, yet concerns about their reliability persist, primarily due to low inter-rater agreement. This study aims to examine factors associated with agreement among peer reviewers in grant evaluations, leveraging data from 134,991 reviews across four Norwegian research funders. Using a cross-classified linear regression model, we will explore the relationship between inter-rater agreement and multiple factors, including reviewer similarity, experience, expertise, research area, application characteristics, review depth, and temporal trends. Our findings are expected to shed light on whether similarity between reviewers (gender, age), their experience, or expertise correlates with higher agreement. Additionally, we investigate whether characteristics of the applications—such as funding amount, research area, or variability in project size—affect agreement levels. By analyzing applications from diverse disciplines and funding schemes, this study aims to provide a comprehensive understanding of the drivers of inter-rater agreement and their implications for grant peer review reliability. The results will inform improvements to peer review processes, enhancing the fairness and validity of funding decisions. All data and analysis scripts will be publicly available, ensuring transparency and reproducibility.