Primer: Journal Impact Factors and Author Rankings

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Without having in-depth knowledge of a particular field of study, it can be hard to figure out which sources of information are most important and influential. For this reason (among others), it’s nice to have a shorthand system that will quickly and accurately identify preeminent publications and authors.

Bibliometrics is the method of using statistical analysis to measure text and information, including measuring the influence and importance of publications and authors. Today we’ll look at a few mainstream metrics that can help you identify the best sources of material for your non-patent literature searches. We’ll also tell you why you shouldn’t believe everything you see…


The dominant statistical measurement for journals is Impact Factor (IF). IF was created by Eugene Garfield and is calculated by Journal Citation Reports (a Thomson Reuters product). This measurement was created (and is now maintained) by the company that would go on to be Thomson Reuters. According to Thomson Reuters, IF is calculated the following way:

A= total cites in 1992
B= 1992 cites to articles published in 1990-91 (this is a subset of A)
C= number of articles published in 1990-91
D= B/C = 1992 impact factor

As you can see, the IF is derived from citation frequency. Citations, the theory goes, are a measure of influence reflected in a particular journal’s field. Certainly, this is true to some extent, and a good starting point. It also helps that IF is a widely held standard of importance–creating a feedback loop of significance and behavior regarding scholarly citations. It should also be noted that there are several twists on this formula to help eliminate influences such as self citation.

On the other hand, Thomson Reuters doesn’t suggest IF to be the end all and be all:

Thomson Reuters does not depend on the impact factor alone in assessing the usefulness of a journal, and neither should anyone else. The impact factor should not be used without careful attention to the many phenomena that influence citation rates, as for example the average number of references cited in the average article. The impact factor should be used with informed peer review.

On the flip side of being such a popular metric, faked journal articles (later retracted) were more likely to appear in leading publications with a high IF.

Eigenfactor score is a more recently developed journal bibliometric. Eigenfactor score derives from weighted citation and citation-of-citation methods. In this way, the calculation of Eigenfactor is more similar to Google’s Pagerank, and more complex than IF. Despite this increase in specificity (and therefore accuracy), Eigenfactor is not the de facto standard that IF has become, and is therefore less influential (meta-influential, if you will).

H-Index is one of the foremost bibliometrics for evaluating the importance and impact of a scholar or author, rather than a journal. H-Index, like IF and Eigenfactor, is based on citations. H-Index is available via several systems, including Scopus, Web of Science, Google Scholar (via the citations-gadget), and Microsoft Academic Search (to name a few). H-Index seeks to provide a bibliometric shorthand for the impact individuals have on their peers, and as such, comparing authors from different fields is not advisable.

As with most, if not all metrics, these metrics can be gamed via various editorial policies such as citation flooding. Be careful not to take any bibliometric as the final word–judge for yourself!

There are many other bibliometrics (and whole areas of study dedicated to such pursuits) out there, but hopefully I’ve helped give you a primer on a few of the more interesting and prevalent ones.

Which bibliometrics do you trust? Have you found them useful as shorthand or at-a-glance information? We’d love to hear your thoughts in the comments below.

Additional reading: I found this NIH Library page about bibliometrics to be a good starting point, including links to more information.

Patent Workbench™ from Landon IP

This post was contributed by Intellogist Team member Chris Jagalla. The Intellogist blog is provided for free by Intellogist’s parent company Landon IP, a major provider of patent searches, trademark searches, technical translations, and information retrieval services.

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