Empirical Evidence for the Relevance of Fractional Scoring in the Calculation of Percentile Rank Scores

TL;DR

This paper provides empirical evidence that fractional scoring improves the accuracy and consistency of percentile rank calculations in bibliometric evaluations, especially with large datasets and tied citation counts.

Contribution

It demonstrates through empirical data that fractional scoring effectively resolves ambiguities in percentile rank calculations, ensuring exact reproduction of theoretical values.

Findings

Fractional scoring aligns total scores with theoretical expectations.

Large datasets with tied citation counts benefit from fractional scoring.

Fractional scoring reduces inconsistencies in percentile rank assignments.

Abstract

Fractional scoring has been proposed to avoid inconsistencies in the attribution of publications to percentile rank classes. Uncertainties and ambiguities in the evaluation of percentile ranks can be demonstrated most easily with small datasets. But for larger datasets an often large number of papers with the same citation count leads to the same uncertainties and ambiguities which can be avoided by fractional scoring. This is demonstrated for four different empirical datasets with several thousand publications each which are assigned to 6 percentile rank classes. Only by utilizing fractional scoring the total score of all papers exactly reproduces the theoretical value in each case.