An evaluation of percentile measures of citation impact, and a proposal for making them better is a research paper published in Scientometrics (2020). On theSindex it has a DataRank of 0.538. It has been cited 35 times.
AbstractPercentiles are statistics pointing to the standing of a paper’s citation impact relative to other papers in a given citation distribution. Percentile Ranks (PRs) often play an important role in evaluating the impact of researchers, institutions, and similar lines of study. BecausePRs are so important for the assessment of scholarly impact, and because citations differ greatly across time and fields, various percentile approaches have been proposed to time- and field-normalize citations. Unfortunately, current popular methods often face significant problems in time- and field-normalization, including when papers are assigned to multiple fields or have been published by more than one unit (e.g., researchers or countries). They also face problems for estimating citation counts for pre-definedPRs (e.g., the 90thPR). We offer a series of guidelines and procedures that, we argue, address these problems and others and provide a superior means to make the use of percentile methods more accurate and informative. In particular, we introduce two approaches,CP-INandCP-EX, that should be preferred in bibliometric studies because they consider the complete citation distribution and can be accurately interpreted. Both approaches are based on cumulative frequencies in percentages (CPs). The paper further shows how bar graphs and beamplots can presentPRs in a more meaningful and accurate manner.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.538
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.