방법 비교

	Source Normalized Impact per Paper (SNIP)	Percentile-Based Citation Impact (PPtop10%)
분야	계량서지학	계량서지학
계열	Process / pipeline	Process / pipeline
기원 연도	2010	2011
창시자	Henk F. Moed; Ludo Waltman, Nees Jan van Eck, Thed van Leeuwen & Martijn Visser	Lutz Bornmann & Loet Leydesdorff; Ludo Waltman & Michael Schreiber
유형	Citation-potential-normalized journal impact pipeline	Distribution-based citation impact pipeline
원전	Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4(3), 265-277.	Leydesdorff, L., & Bornmann, L. (2011). Integrated impact indicators compared with impact factors: An alternative research design with policy implications. Journal of the American Society for Information Science and Technology, 62(11), 2133-2146.
별칭	SNIP, Citation Potential Normalization, Source-Normalized Journal Impact, Field-Normalized Citations per Paper	Percentile Rank Citation Indicators, Top 10% Highly Cited Papers Indicator, PPtop10%, Integrated Impact Indicator (I3)
관련	3	3
요약	The Source Normalized Impact per Paper, or SNIP, corrects a journal's citation rate for the citation behavior of its field so that journals in heavily cited and lightly cited disciplines can be compared on the same scale. Henk Moed introduced SNIP in 2010 with a distinctive twist: rather than classifying journals into predefined subject categories, it defines a journal's field from the bottom up as the set of papers that actually cite it, and it normalizes by that field's citation potential, measured from how long the citing papers' reference lists are. Fields whose authors cite many references generate more citations to go around, so a raw citation rate means different things in mathematics than in molecular biology. SNIP divides raw impact per paper by this citation potential to produce a field-corrected indicator. Ludo Waltman and colleagues revised the original formula in 2013 to remove some counterintuitive properties and improve stability; the revised SNIP is the version distributed in Scopus.	Percentile-based citation impact replaces the average citation count with a paper's rank position within a properly defined reference set. Instead of asking how many citations a paper received, it asks where the paper falls in the citation distribution of comparable papers from the same field, year, and document type. Because citation distributions are extremely skewed, a single highly cited paper can inflate a mean, so Lutz Bornmann and Loet Leydesdorff argued that impact should be measured non-parametrically through percentile ranks and the share of papers reaching the top of their field. The most widely used summary is PPtop10%, the proportion of a unit's papers that belong to the most-cited 10% of their reference set; Leydesdorff and Bornmann's Integrated Impact Indicator (I3) generalizes this idea by integrating the full percentile curve. Ludo Waltman and Michael Schreiber clarified how percentile ranks should be computed when many papers share the same citation count.