Archive for citation index

statistical modelling of citation exchange between statistics journals

Posted in Books, Statistics, University life with tags , , , , , on April 10, 2015 by xi'an

Cristiano Varin, Manuela Cattelan and David Firth (Warwick) have written a paper on the statistical analysis of citations and index factors, paper that is going to be Read at the Royal Statistical Society next May the 13th. And hence is completely open to contributed discussions. Now, I have written several entries on the ‘Og about the limited trust I set to citation indicators, as well as about the abuse made of those. However I do not think I will contribute to the discussion as my reservations are about the whole bibliometrics excesses and not about the methodology used in the paper.

The paper builds several models on the citation data provided by the “Web of Science” compiled by Thompson Reuters. The focus is on 47 Statistics journals, with a citation horizon of ten years, which is much more reasonable than the two years in the regular impact factor. A first feature of interest in the descriptive analysis of the data is that all journals have a majority of citations from and to journals outside statistics or at least outside the list. Which I find quite surprising. The authors also build a cluster based on the exchange of citations, resulting in rather predictable clusters, even though JCGS and Statistics and Computing escape the computational cluster to end up in theory and methods along Annals of Statistics and JRSS Series B.

In addition to the unsavoury impact factor, a ranking method discussed in the paper is the eigenfactor score that starts with a Markov exploration of articles by going at random to one of the papers in the reference list and so on. (Which shares drawbacks with the impact factor, e.g., in that it does not account for the good or bad reason the paper is cited.) Most methods produce the Big Four at the top, with Series B ranked #1, and Communications in Statistics A and B at the bottom, along with Journal of Applied Statistics. Again, rather anticlimactic.

The major modelling input is based on Stephen Stigler’s model, a generalised linear model on the log-odds of cross citations. The Big Four once again receive high scores, with Series B still much ahead. (The authors later question the bias due to the Read Paper effect, but cannot easily evaluate this impact. While some Read Papers like Spiegelhalter et al. 2002 DIC do generate enormous citation traffic, to the point of getting re-read!, other journals also contain discussion papers. And are free to include an on-line contributed discussion section if they wish.) Using an extra ranking lasso step does not change things.

In order to check the relevance of such rankings, the authors also look at the connection with the conclusions of the (UK) 2008 Research Assessment Exercise. They conclude that the normalised eigenfactor score and Stigler model are more correlated with the RAE ranking than the other indicators.  Which means either that the scores are good predictors or that the RAE panel relied too heavily on bibliometrics! The more global conclusion is that clusters of journals or researchers have very close indicators, hence that ranking should be conducted with more caution that it is currently. And, more importantly, that reverting the indices from journals to researchers has no validation and little information.

Citation abuses

Posted in Statistics with tags , , , , , , on October 21, 2009 by xi'an

“There is a belief that citation statistics are
inherently more accurate because they
substitute simple numbers for complex
judgments, and hence overcome the
possible subjectivity of peer review.
But this belief is unfounded.”

A very interesting report appeared in the latest issue of Statistical Science about bibliometrics and its abuses (or “bibliometrics as an abuse per se”!). It was commissioned by the IMS, the IMU and the ICIAM. Along with the set of comments (by Bernard Silverman, David Spiegelhalter, Peter Hall and others) also posted in arXiv, it is a must-read!

“even a casual inspection of the h-index and its variants shows
that these are naïve attempts to understand complicated citation
records. While they capture a small amount of information about
the distribution of a scientist’s citations, they lose crucial
information that is essential for the assessment of research.”

The issue is not gratuitous. While having Series B ranked with a high impact factor is an indicator of the relevance of a majority of papers published in the journal, there are deeper and more important issues at stake. Our grant allocations, our promotions, our salary are more and more dependent on these  “objective” summary or “comprehensive” factors. The misuse of bibliometrics stems from government bodies and other funding agencies wishing to come up with assessments of the quality of a researcher that bypass peer reviews and, more to the point, are easy to come by.

The report points out the many shortcomings of journal impact factors. Its two-year horizon is very short-sighted in mathematics and statistics. As an average, it is strongly influenced by outliers, like controversial papers or broad surveys, as shown by the yearly variations of the thing. Commercial productions like Thomson’s misses a large part of the journals that could quote a given paper and this is particularly true for fields at the interface between disciplines and for emergent topics. The variation in magnitude between disciplines is enormous and based on the impact factor I’d rather publish one paper in Bioinformatics than four in the Annals of Statistics… The second issue is that the “quality” of the journal does not automatically extend to all papers it publishes: multiplying papers by the journal impact factor is thus ignoring variation to an immense extent. The report illustrates this with the fact that a paper published in a journal with half the impact factor of another journal has a 62% probability to be more quoted than if it had been published in this other journal! The h-factor is similarly criticised by the report.  More fundamentally, the report also analyses the multicriteria nature of citations, which cannot be reflected (only) as a measure of worth of the quoted papers.