percentile ranks in excellence and impact indicators

Loet Leydesdorff loet at LEYDESDORFF.NET
Tue Apr 10 01:31:12 EDT 2012

Accounting for the Uncertainty in the Evaluation of Percentile Ranks

In a recent paper entitled "Inconsistencies of Recently Proposed Citation
Impact Indicators and how to Avoid Them," Schreiber (2012, at
arXiv:1202.3861 <> ) proposed (i) a method to
assess tied ranks consistently and (ii) fractional attribution to percentile
ranks in the case of relatively small samples (e.g., for n < 100).
Schreiber's solution to the problem of how to handle tied ranks is
convincing, in my opinion. The fractional attribution, however, is
computationally intensive and cannot be done manually for even moderately
large batches of documents. Schreiber attributed scores fractionally to the
six percentile rank classes used in the Science and Engineering Indicators
of the U.S. National Science Board, and thus missed, in my opinion, the
point that fractional attribution at the level of hundred percentiles--or
equivalently quantiles as the continuous random variable--is only a linear,
and therefore much less complex problem. Given the quantile-values, the
non-linear attribution to the six classes or any other evaluation scheme is
then a question of aggregation. A new routine based on these principles
(including Schreiber's solution for tied ranks) is made available as
software for the assessment of documents retrieved from the Web of Science
(at this http URL <> ). 


** forthcoming in JASIST

** apologies for cross-postings


Loet Leydesdorff 

University of Amsterdam
Amsterdam School of Communications Research (ASCoR)
Kloveniersburgwal 48, 1012 CX Amsterdam.
 <mailto:loet at> loet at ;
<> &hl=en  


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