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Journal-level citation metrics | Editor Resources


Journal-level citation metrics

An introduction to metrics and their use

Since the start of the twentieth century metrics have become an
increasingly important part of both journal publishing and researcher
evaluation. This is the first in a short series of posts on metrics,
giving an overview of what they measure, why we use them, and some of
their issues. I am addressing the metrics in order of age, starting with
citations; future posts will discuss usage, article level metrics, and

Historically citation metrics have been the standard, and only, tool
available to systematically evaluate journals and articles. This started
in the 1960s when Dr. Eugene Garfield founded the Institute for
Scientific Information (ISI) by using computers to create the first
systematic, multilingual, and multidisciplinary abstracting database.
However, citation metrics really came to the fore in the 1970s when the
Impact Factor was published as part of the Journal Citation Reports® (JCR).
The Impact Factor was initially designed to be used by librarians as an
aid to collection management, but over time it has become a proxy value
for journal quality.

Conceptually, the Impact Factor is a very simple metric - the average
number of citations received by articles in a journal within a
timeframe. In more detail, the formula used to calculate the Impact
Factor is:

Number of citations received in 2012 to 2010 and 2011 content

Number of articles and reviews published in 2010 and 2011
This simplicity creates a range of issues around the Impact Factor, and I will touch on three of the most important.

Distribution of citations

The Impact Factor is an arithmetic mean and does not adjust for the
distribution of citations to articles. One highly cited article can have
a major positive effect on the Impact Factor. The most extreme example
of this relates to the article “A short history of Shelx,” published in Acta Crystallographica Section A in 2008 which to
date has been cited more than 33,000 times. The 2008 Impact Factor of
2.051 did not include citations to this article, whereas citations to
the article were included in the 2009 Impact Factor, causing it to rise
to 49.926, the second highest in the entire JCR for that year; similarly
the 2010 Impact Factor was 54.333. This article was no longer included
after this and the 2011 Impact Factor dropped to 2.076. Even in a large,
high-prestige journal such as Nature, the top 1% of articles accounted for 6.5% of the citations in the 2012 Impact Factor calculation.

Source vs. non-source items

The methodology used by Thomson Reuters to compile the JCR and Impact
Factors was created in the 1970s and the JCR only analyzes citations
for journal, year, and volume and the phrase “in print”; no other
information is used. This means that the JCR cannot distinguish between
citations to articles, reviews, or editorials. So that the Impact Factor
doesn’t penalize journals for publishing content such as book reviews,
editorials, and news items, which are infrequently cited, these article
types are not counted in the denominator of the Impact Factor, but
citations to this content are still counted.

There are two issues stemming from this. Firstly, the classification
of content is subjective; not every journal has the same content treated
the same way and, although Thomson Reuters provides guidance on how
they decide what counts, content such as extended abstracts and author
commentaries fall into a gray area. Secondly, these free articles are
cited so they inflate the Impact Factor without any offset in the
denominator of the equation.

Subject areas and research type

Different subject areas have very different citation patterns which
are reflected in their Impact Factors. The aggregate Impact Factor of
the cell biology category in the 2012 JCR was 5.734, for internal &
general medicine it was 3.839, and for mathematics it was 0.716. In the
social sciences several psychology categories have aggregate Impact
Factors higher than 2, compared to history’s aggregate Impact Factor of

This does not mean that cell biology research is better than medicine
or history, but is merely a reflection of the differing citation
patterns, database coverage and dominance of journals between the
disciplines. Differences in Impact Factor also exist between basic,
applied, practitioner, and educational journals. For more details on
these issues please see Jo Cross’s “Impact Factors back to basics” Editors’ Bulletin article.

Other metrics

Partly because of some of these issues, a large number of other
journal-based metrics have been created, based either on the Web of
Science or the Scopus databases. The details of the JCR-based metrics
have been covered in “Citations and the Impact Factor” and Source Normalized Impact per Paper (SNIPs) in “Are Impact Factors facing the SNIP?”

Table 1 Correlation coefficients of various metrics. 2012 JCR compared against 2012 SJR and 2012 SNIP


Table 1 shows the correlation coefficients of seven journal-level
metrics based either on the Web of Science or Scopus. A score of 1 means
that they follow exactly the same order; a score of zero means there is
no correlation. The metrics fall into three groups: total cites and
Eigenfactor, which are not adjusted for journal size; Impact Factor,
5-year Impact Factor, Article Influence Score and SJR which are adjusted
for journal size; and finally, the SNIP, which is loosely correlated
with the others.

This means that if a journal has a high Impact Factor it is likely to
perform well by the related metrics; likewise if a journal has a high
total citation count it is likely to do well using the Eigenfactor.
These extra journal-level citation metrics do not provide very much
extra information, they just give another chance for each journal to
claim to be top in its subject.

Published: December 12, 2013 | Author:

James Hardcastle,

Research Manager

| Category: Citations, impact and usage, Front page |
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Journal-level citation metrics | Editor Resources

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