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Percentile-based approaches have been proposed as a non-parametricalternative to parametric central-tendency statistics to normalize observedcitation counts. Percentiles are based on an ordered set of citation counts ina reference set, whereby the fraction of papers at or below the citation countsof a focal paper is used as an indicator for its relative citation impact inthe set. In this study, we pursue two related objectives: (1) althoughdifferent percentile-based approaches have been developed, an approach ishitherto missing that satisfies a number of criteria such as scaling of thepercentile ranks from zero (all other papers perform better) to 100 (all otherpapers perform worse), and solving the problem with tied citation ranksunambiguously. We introduce a new citation-rank approach having theseproperties, namely P100. (2) We compare the reliability of P100 empiricallywith other percentile-based approaches, such as the approaches developed by theSCImago group, the Centre for Science and Technology Studies (CWTS), andThomson Reuters (InCites), using all papers published in 1980 in ThomsonReuters Web of Science (WoS). How accurately can the different approachespredict the long-term citation impact in 2010 (in year 31) using citationimpact measured in previous time windows (years 1 to 30)? The comparison of theapproaches shows that the method used by InCites overestimates citation impact(because of using the highest percentile rank when papers are assigned to morethan a single subject category) whereas the SCImago indicator shows higherpower in predicting the long-term citation impact on the basis of citationrates in early years. Since the results show a disadvantage in this predictiveability for P100 against the other approaches, there is still room for furtherimprovements.

Which percentile-based approach should be preferred for calculating  normalized citation impact values? An empirical comparison of five approaches  including a newly developed citation-rank approach (P100)