Analysis can be fun – A Case Study in Bibliometrics

So, after my previous article telling a little bit about bibliometrics, I thought I would take the opportunity to get a little more in-depth.  I have taken one of my presentations and removed all identifying references.  It should be noted that the presentation was also accompanied by a much larger Excel file, containing all of the raw data, as well as copious strategic suggestions in the “Notes” sections of each slide.  I have also mixed and matched slides from different presentations to help ensure anonymity.



Here we have a simple chart, showing the Impact Factor and the Total Cites for a given journal over a period of 9 years.  So in 2009, this journal received a total of 18,455 citations from all other journals indexed by ISI/Thomson.  The journal had an Impact Factor of 4.077, which means:  the journal received an average of more than 4 cited references per article for each article written in the two years after the year of publication.

It’s kind of complicated, and somewhat arbitrary actually, but every library, every publisher, and even most publishing authors are well aware of journals’ Impact Factor, but not exactly what they mean, or even represent.  So let me try to explain.

In 2009, our journal received over 18,000 citations.  (Actually, they probably received more, but if you think back to our previous lesson, a journal doesn’t really “count” unless it is indexed by ISI and included in the Journal Citation Reports, thusly receiving an all-important Impact Factor.)  Many – possibly the majority, we shall see later – were citations to articles published a long, long time ago.  Many more were citations to articles published very recently.  But some journals are larger than others – another journal may have received a total of 50,000 citations in 2009, but what if they product weekly issues vs the quarterly issues our journal publishes?  This is where the Impact Factor comes in.

Presumably, if a journal publishes a good article, it will have a lot of influence in the marketplace, a great impact.  Dr. Garfield designed the simple ratio # Citations (during a given year)/# Citable Articles (during the two years prior to the IF year).  Now, arguments still echo in the ListServs about what constitutes a citable article, and why exactly is the given time frame for the denominator those specific years?  (The latter is basically because it showed the most impressive Impact Factor for life sciences disciplines, which was the bread and butter of ISI.  They have since developed a 5-year Impact Factor, and other metrics, to account for this oddity.)

So, back to our example above.  There were more than 18,000 citations to our journal in 2009.  The Impact Factor of 4.077 tells us that the journal, as a whole, received, on average, a little more than 4 citations for every item written in 2007 and 2008.  Citations to articles published in 2009 don’t count, nor do citations to articles published before 2007.  (Again, we’ll see many other metrics which account for this.)

So now publishers, editors, writers, librarians and many others can all see this Impact Factor.  Those in the industry might say this is good or bad, depending on exactly what industry they are in, what discipline the journal is in, and what exactly the party wants to do with the information.  It is just a number, after all, and unless it’s put into context, it is meaningless. 

But the whole marketplace uses this metric (and primarily, only this metric) to evaluate a journal.  They extrapolate, for example, that publishing in this journal will get an author about 4 citations, guaranteed, with a margin of error, for their article.  As we will see, looking at other metrics too, and then how they all interplay with each other (and what they mean in the larger context) is even more important.

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