How to analyse percentile impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes and top-cited papers

TL;DR

This paper discusses how to analyze percentile-based bibliometric data effectively, focusing on distribution analysis and impact range evaluation to improve scientific performance assessments.

Contribution

It introduces methods for meaningful analysis of percentile data in bibliometrics, emphasizing distribution and impact range considerations beyond simple percentile calculation.

Findings

Comparison of four university publication sets demonstrates analysis methods.

Distribution of percentiles reveals differences in citation impact.

Focus on top-cited papers enhances evaluation accuracy.

Abstract

According to current research in bibliometrics, percentiles (or percentile rank classes) are the most suitable method for normalising the citation counts of individual publications in terms of the subject area, the document type and the publication year. Up to now, bibliometric research has concerned itself primarily with the calculation of percentiles. This study suggests how percentiles can be analysed meaningfully for an evaluation study. Publication sets from four universities are compared with each other to provide sample data. These suggestions take into account on the one hand the distribution of percentiles over the publications in the sets (here: universities) and on the other hand concentrate on the range of publications with the highest citation impact - that is, the range which is usually of most interest in the evaluation of scientific performance.