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The misuse of citation statistics

"While it is incorrect to say that the impact factor gives no information about individual papers in a journal, the information is surprisingly vague and can be dramatically misleading" says a recent report published by the Joint Committee on Quantitative Assessment of Research {1}.

We loved this highly informative report detailing how statistics such as the impact factor and h-index are misused, not well understood and not well studied. Robert Adler and his colleagues aim to point out the limitations of using citation statistics, as well as how to make better use of them.

This superb article also calls into question the accuracy, objectivity and simplicity of using such statistics as the sole measure of research quality:

"We do not dismiss citation statistics as a tool for assessing the quality of research-citation data and statistics can provide some valuable information ... But citation data provide only a limited and incomplete view of research quality, and the statistics derived from citation data are sometimes poorly understood and misused. Research is too important to measure its value with only a single coarse tool."

We agree - that's why the F1000 Factor is so useful. It provides an alternative measure to the impact factor and is based on the opinion of eminent members of the medical community, irrespective of what journal the article comes from.

The Adler et al paper is receiving a lot of attention and is yet more evidence adding to the ever-growing base that our reliance on, and trust of, such statistics may need to be re-assessed.

Read more about the F1000 Factor here.
 

References:

1. http://www.mathunion.org/fileadmin/IMU/Report/CitationStatistics.pdf 

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Posted by Sean Porazinski on Wednesday Jul 02, 2008, Category: Impact factor versus... | Permalink