Anyone that maintains a website is happy that people out there are interested enough to visit. Web traffic is one thing, but I take greatest pleasure in seeing quantixed posts being cited in academic papers.
I love the fact that some posts on here have been cited in the literature more than some of my actual papers.
It’s difficult to track citations to web resources. This is partly my fault, I think it is possible to register posts so that they have a DOI, but I have not done this and so tracking is a difficult task. Websites are part of what is known as the grey literature: items that are not part of traditional academic publishing.
The most common route for me to discover that a post has been cited is when I actually read the paper. There are four examples that spring to mind: here, here, here and here. With these papers, I read the paper and was surprised to find quantixed cited in the bibliography.
Vanity and curiosity made me wonder if there were other citations I didn’t know about. A cited reference search in Web of Science pulled up two more: here and here.
A bit of Googling revealed yet more citations, e.g. two quantixed posts are cited in this book. And another citation here.
OK so quantixed is not going to win any “highly cited” prizes or develop a huge H-index (if something like that existed for websites). But I’m pleased that 1) there are this many citations given that there’s a bias against citing web resources, and 2) the content here has been useful to others, particularly for academic work.
All of these citations are to posts looking at impact factors, metrics and publication lag times. In terms of readership, these posts get sustained traffic, but currently the most popular posts on quantixed are the “how to” guides, LaTeX to Word and Back seeing the most traffic. Somewhere in between citation and web traffic are cases when quantixed posts get written about elsewhere, e.g. in a feature in Nature by Kendall Powell.
The post title comes from “Five Get Over Excited” by The Housemartins. A band with a great eye for song titles, it can be found on the album “The People Who Grinned Themselves to Death”.
I have written previously about Journal Impact Factors (here and here). The response to these articles has been great and earlier this year I was asked to write something about JIFs and citation distributions for one of my favourite journals. I agreed and set to work.
Things started off so well. A title came straight to mind. In the style of quantixed, I thought The Number of The Beast would be amusing. I asked for opinions on Twitter and got an even better one (from Scott Silverman @sksilverman) Too Many Significant Figures, Not Enough Significance. Next, I found an absolute gem of a quote to kick off the piece. It was from the eminently quotable Sydney Brenner.
Before we develop a pseudoscience of citation analysis, we should remind ourselves that what matters absolutely is the scientific content of a paper and that nothing will substitute for either knowing it or reading it.
That quote was from a Loose Ends piece that Uncle Syd penned for Current Biology in 1995. Wow, 1995… that is quite a few years ago I thought to myself. Never mind. I pressed on.
There’s a lot of literature on JIFs, research assessment and in fact there are whole fields of scholarly activity (bibliometrics) devoted to this kind of analysis. I thought I’d better look back at what has been written previously. The “go to” paper for criticism of JIFs is Per Seglen’s analysis in the BMJ, published in 1997. I re-read this and I can recommend it if you haven’t already seen it. However, I started to feel uneasy. There was not much that I could add that hadn’t already been said, and what’s more it had been said 20 years ago.
Around about this time I was asked to review some fellowship applications for another EU country. The applicants had to list their publications, along with the JIF. I found this annoying. It was as if SF-DORA never happened.
There have been so many articles, blog posts and more written on JIFs. Why has nothing changed? It was then that I realised that it doesn’t matter how many things are written – however coherently argued – people like JIFs and they like to use them for research assessment. I was wasting my time writing something else. Sorry if this sounds pessimistic. I’m sure new trainees can be reached by new articles on this topic, but acceptance of JIF as a research assessment tool runs deep. It is like religious thought. No amount of atheist writing, no matter how forceful, cogent, whatever, will change people’s minds. That way of thinking is too deeply ingrained.
As the song says, “If I can’t change your mind, then no-one will”.
So I declared defeat and told the journal that I felt like I had said all that I could already say on my blog and that I was unable to write something for them. Apologies to all like minded individuals for not continuing to fight the good fight.
But allow me one parting shot. I had a discussion on Twitter with a few people, one of whom said they disliked the “JIF witch hunt”. This caused me to think about why the JIF has hung around for so long and why it continues to have support. It can’t be that so many people are statistically illiterate or that they are unscientific in choosing to ignore the evidence. What I think is going on is a misunderstanding. Criticism of a journal metric as being unsuitable to judge individual papers is perceived as an attack on journals with a high-JIF. Now, for good or bad, science is elitist and we are all striving to do the best science we can. Striving for the best for many scientists means aiming to publish in journals which happen to have a high JIF. So an attack of JIFs as a research assessment tool, feels like an attack on what scientists are trying to do every day.
Because of this intense focus on high-JIF journals… what people don’t appreciate is that the reality is much different. The distribution of JIFs is as skewed as that for the metric itself. What this means is that focussing on a minuscule fraction of papers appearing in high-JIF journals is missing the point. Most papers are in journals with low-JIFs. As I’ve written previously, papers in journals with a JIF of 4 get similar citations to those in a journal with a JIF of 6. So the JIF tells us nothing about citations to the majority of papers and it certainly can’t predict the impact of these papers, which are the majority of our scientific output.
So what about those fellowship applicants? All of them had papers in journals with low JIFs (<8). The applicants’ papers were indistinguishable in that respect. What advice would I give to people applying to such a scheme? Well, I wouldn’t advise not giving the information asked for. To be fair to the funding body they also asked for number of citations for each paper, but for papers that are only a few months old, this number is nearly always zero. My advice would be to try and make sure that your paper is available freely for anyone to read. Many of the applicants’ papers were outside my expertise and so the title and abstract didn’t tell me much about the significance of the paper. So I looked at some of these papers to look at the quality of the data in there… if I had access. Applicants who had published in closed access journals are at a disadvantage here because if I couldn’t download the paper then it was difficult to assess what they had been doing.
I was thinking that this post would be a meta-meta-blogpost. Writing about an article which was written about something I wrote on my blog. I suppose it still is, except the article was never finished. I might post again about JIFs, but for now I doubt I will have anything new to say that hasn’t already been said.
The post title is taken from “If I Can’t Change Your Mind” by Sugar from their LP Copper Blue. Bob Mould was once asked about song-writing and he said that the perfect song was like a maths puzzle (I can’t find a link to support this, so this is from memory). If you are familiar with this song, songwriting and/or mathematics, then you will understand what he means.
Edit @ 08:22 16-05-20 I found an interview with Bob Mould where he says song-writing is like city-planning. Maybe he just compares song-writing to lots of different things in interviews. Nonetheless I like the maths analogy.
I was interested in the analysis by Frontiers on the lack of a correlation between the rejection rate of a journal and the “impact” (as measured by the JIF). There’s a nice follow here at Science Open. The Times Higher Education Supplement also reported on this with the line that “mass rejection of research papers by selective journals in a bid to achieve a high impact factor is an enormous waste of academics’ time”.
First off, the JIF is a flawed metric in a number of ways but even at face value, what does this analysis really tell us?
This plot is taken from the post by Jon Tennant at Science Open.
As others have pointed out:
The rejection rate is dominated by desk rejects, which although very annoying, don’t take that much time.
Without knowing the journal name it is difficult to know what to make of the plot.
The data are available from Figshare and – thanks to Thomson-Reuters habit of reporting JIF to 3 d.p. – we can easily pull the journal titles from a list using JIF as a key. The list is here. Note that there may be errors due to this quick-and-dirty method.
The list takes on a different meaning when you can see the Journal titles alongside the numbers for rejection rate and JIF.
Looking for familiar journals – whichever field you are in – you will be disappointed. There’s an awful lot of noise in there. By this, I mean journals that are outside of your field.
This is the problem with this analysis as I see it. It is difficult to compare Nature Neuroscience with Mineralium Deposita…
My plan with this dataset was to replot rejection rate versus JIF2014 for a few different journal categories, but I don’t think there’s enough data to do this and make a convincing case one way or the other. So, I think the jury is still out on this question.
It would be interesting to do this analysis on a bigger dataset. Journals releasing their numbers on rejection rates would be a step forward to doing this.
One final note:
The Orthopedic Clinics of North America is a tough journal. Accepts only 2 papers in every 100 for an impact factor of 1!
The post title is from “Throes of Rejection” by Pantera from their Far Beyond Driven LP. I rejected the title “Satan Has Rejected my Soul” by Morrissey for obvious reasons.
It’s a great illustration of why reporting means for skewed distributions is a bad idea. And this brings us quickly to Thomson-Reuters’ Journal Impact Factor (JIF).
I can actually remember the first time I realised that the JIF was a spurious metric. This was in 2003, after reading a letter to Nature from David Colquhoun who plotted out the distribution of citations to a sample of papers in Nature. Up until that point, I hadn’t appreciated how skewed these data are. We put it up on the lab wall.
Now, the JIF for a given year is calculated as follows:
A JIF for 2013 is worked out by counting the total number of 2013 cites to articles in that journal that were published in 2011 and 2012. This number is divided by the number of “citable items” in that journal in 2011 and 2012.
There are numerous problems with this calculation that I don’t have time to go into here. If we just set these aside for the moment, the JIF is still used widely today and not for the purpose it was originally intended. Eugene Garfield, created the metric to provide librarians with a simple way to prioritise subscriptions to Journals that carried the most-cited scientific papers. The JIF is used (wrongly) in some institutions in the criteria for hiring, promotion and firing. This is because of the common misconception that the JIF is a proxy for the quality of a paper in that journal. Use of metrics in this manner is opposed by the SF-DORA and I would encourage anyone that hasn’t already done so, to pledge their support for this excellent initiative.
Why not report the median rather than the mean?
With the citation distribution in mind, why do Thomson-Reuters calculate the mean rather than the median for the JIF? It makes no sense at all. If you didn’t quite understand why from the @statfact tweet above, then look at this:
The Acta Crystallographica Section A effect. The plot shows that this journal had a JIF of 2.051 in 2008 which jumped to 49.926 in 2009 due to a single highly-cited paper. Did every other paper in this journal suddenly get amazingly awesome and highly-cited for this period? Of course not. The median is insensitive to outliers like this.
The answer to why Thomson-Reuters don’t do this is probably for ease of computation. The JIF (mean) requires only three numbers for each journal, whereas calculating the median would require citation information for each paper under consideration for each journal. But it’s not that difficult (see below). There’s also a mismatch in the items that bring in citations to the numerator and those that count as “citeable items” in the denominator. This opacity is one of the major criticisms of the Impact Factor and this presents a problem for them to calculate the median.
Let’s crunch some citation numbers
I had a closer look at citation data for a small number of journals in my field. DC’s citation distribution plot was great (in fact, superior to JIF data) but it didn’t capture the distribution that underlies the JIF. I crunched the IF2012 numbers (released in June 2013) sometime in December 2013. This is shown below. My intention was to redo this analysis more fully in June 2014 when the IF2013 was released, but I was busy, had lost interest and the company said that they would be more open with the data (although I’ve not seen any evidence for this). I wrote about partial impact factors instead, which took over my blog. Anyway, the analysis shown here is likely to be similar for any year and the points made below are likely to hold.
I mainly looked at Nature, Nature Cell Biology, Journal of Cell Biology, EMBO Journal and J Cell Science. Using citations in 2012 articles to papers published in 2010 and 2011, i.e. the same criteria as for IF2012.
The first thing that happens when you attempt this analysis is that you realise how unreproducible the Thomson-Reuters JIFs are. This has been commented on in the past (e.g. here), yet I had the same data as the company uses to calculate JIFs and it was difficult to see how they had arrived at their numbers. After some wrangling I managed to get a set of papers for each journal that gave close to the same JIF.
From this we can look at the citation distribution within the dataset for each journal. Below is a gallery of these distributions. You can see that the data are highly skewed. For example, JCB has kurtosis of 13.5 and a skewness of 3. For all of these journals ~2/3 of papers had fewer than the mean number of citations. With this kind of skew, it makes more sense to report the median (as described above). Note that Cell is included here but was not used in the main analysis.
So how do these distributions look when compared? I plotted each journal compared to JCB. They are normalised to account for the differing number of papers in each dataset. As you can see they are largely overlapping.
If the distributions overlap so much, how certain can we be that a paper in a journal with a high JIF will have more citations than a paper in a journal with a lower JIF? In other words, how good is the JIF (mean or median) at predicting how many citations a paper published in a certain journal is likely to have?
To look at this, I ran a Monte Carlo analysis comparing a random paper from one journal with a random one from JCB and looked at the difference in number of citations. Papers in EMBO J are indistinguishable from JCB. Papers in JCS have very slightly fewer citations than JCB. Most NCB papers have a similar number of cites to papers in JCB, but there is a tail of papers with higher cites, a similar but more amplified picture for Nature.
Thomson-Reuters quotes the JIF to 3 d.p. and most journals use this to promote their impact factor (see below). The precision of 3 d.p. is ridiculous when two journals with IFs of 10.822 and 9.822 are indistinguishable when it comes to the number of citations to randomly sampled papers in that journal.
So how big do differences in JIF have to be in order to be able to tell a “Journal X paper” from a “Journal Y paper” (in terms of citations)?
To look at this I ran some comparisons between the journals in order to get some idea of “significant differences”. I made virtual issues of each journal with differing numbers of papers (5,10,20,30) and compared the citations in each via Wilcoxon rank text and then plotted out the frequency of p-values for 100 of these tests. Please leave a comment if you have a better idea to look at this. I liked this method over the head-to-head comparison for two papers as it allows these papers the benefit of the (potential) reflected glory of other papers in the journal. In other words, it is closer to what the JIF is about.
OK, so this shows that sufficient sample size is required to detect differences, no surprise there. But at N=20 and N=30 the result seems pretty clear. A virtual issue of Nature trumps a virtual issue of JCB, and JCB beats JCS. But again, there is no difference between JCB and EMBO J. Finally, only ~30% of the time would a virtual issue of NCB trump JCB for citations! NCB and JCB had a difference in JIF of almost 10 (20.761 vs 10.822). So not only is quoting the JIF to 3 d.p. ridiculous, it looks like rounding the JIF to the nearest 5 (or 10) might be better!
This analysis supports the idea that there are different tiers of journal (in Cell Biology at least). But the JIF is the bluntest of tools to separate these journals. A more rigorous analysis is needed to demonstrate this more clearly but it is not feasible to do this while having a dataset which agrees with that of Thomson-Reuters (without purchasing the data from the company).
If you are still not convinced about how shortcomings of the JIF, here is a final example. The IF2013 for Nature increased from 38.597 to 42.351. Let’s have a look at the citation distributions that underlie this rise of 3.8! As you can see below they are virtually identical. Remember that there’s a big promotion that the journal uses to pull in new subscribers, seems a bit hollow somehow doesn’t it? Disclaimer: I think this promotion is a bit tacky, but it’s actually a really good deal… the News stuff at the front and the Jobs section at the back alone are worth ~£40.
Show us the data!
Recently, Stephen Curry has called for Journals to report the citation distribution data rather than parroting their Impact Factor (to 3 d.p.). I agree with this. The question is though – what to report?
The IF window is far too narrow (2 years + 1 year of citations) so a broader window would be more useful.
A comparison dataset from another journal is needed in order to calibrate ourselves.
Citations are problematic – not least because they are laggy. A journal could change dramatically and any citation metric would not catch up for ~2 years.
Related to this some topics are hot and others not. I guess we’re most interested in how a paper in Journal X compares to others of its kind.
Any information reported needs to be freely available for re-analysis and not in the hands of a company. Google Scholar is a potential solution but it needs to be more open with its data. They already have a journal ranking which provides a valuable and interesting alternative view to the JIF.
One solution would be to show per article citation profiles comparing these for similar papers. How do papers on a certain topic in Journal X compare to not only those in Journal Y but to the whole field? In my opinion, this metric would be most useful when assessing scholarly output.
Thanks for reading to the end (or at least scrolling all the way down). The take home points are:
the JIF is based on highly skewed data.
the median rather than the mean is better for summarising such distributions.
JIF is a very poor indicator of the number of citations a random paper in the journal received!
reporting a JIF to 3 d.p. is ridiculous, it would be better to round to the nearest 5 or 10.
an open resource for comparing citation data per journal would be highly valuable.
The post title is taken from “Wrong Number” by The Cure. I’m not sure which album it’s from, I only own a Greatest Hits compilation.