Assessing Scholarly Productivity: The Numbers Game
Assessing Scholarly Productivity
To evaluate the work of scholars objectively, funding agencies and tenure committees may attempt to quantify both its quality and impact. Quantifying scholarly work is fraught with danger, but the current emphasis on assessment in academe suggests that such measures can only become more important. There are a number of descriptive statistics associated with scholarly productivity. These fall broadly into two categories: those that describe individual researchers and those that describe journals.
Raw Citation Counts
One way to measure the impact of a paper is to simply count how many times it has been cited by others. This can be accomplished by finding the paper in Google Scholar and noting the "Cited by" value beneath the citation. Such numbers may be added together, or perhaps averaged over a period of years, to provide an informal assessment of scholarly productivity. Better yet, use Google Scholar Citations to keep a running list of your publications and their "cited by" numbers. For more information on determining where, by whom, and how often an article has been cited, see IC Library's guide on Cited Reference Searching.
The h-index, created by Jorge E. Hirsh of the University of California, San Diego, is described by its creator as follows:
A scientist has index h if h of his/her Np papers have at least h citations each, and the other (Np - h) papers have no more than h citations each.1
In other words, if I have an h-index of 5, that means that my five most-cited papers each have been cited five or more times. This can be visualized by a graph, on which each point represents a paper. The scholar's papers are ranked along the x-axis by decreasing number of citing papers, while the actual number of citing papers is shown by the point's position along the y-axis. The grey line represents the equality of paper rank and number of citating articles. The h-index is equal to the number of points above the grey line.
The value of h will depend on the database used to calculate it. 2 Thomson Reuter's Web of Science and Elsevier's Scopus (neither is available at IC) offer automated tools for calculating this value. In November of 2011, Google Scholar Citations became generally available. This will calculate h based on the Google Scholar database. An add-on for Firefox called the Scholar H-Index Calculator is also based on Google Scholar data.
Comparisons of h are only valid within a discipline, since standards of productivity vary widely between fields. Researchers in the life sciences, for instance, will generally have higher h values than those in physics.1
A large number of modifications to the h-index have been proposed, many attempting to correct for factors such as length of career and co-authorship.
Rightly or wrongly, the quality of a paper is sometimes judged by the reputation of the journal in which it is published. Various metrics have been devised to describe the importance of a journal.
The Impact Factor (IF) is a proprietary measure calculated annually by Thomson Reuters (formerly by ISI). This figure is based on how often papers published in a given journal in the preceding two years are cited during the current year. This number is divided by the number of "citable items" published by that journal during the preceding two years to arrive at the IF. Weaknesses of this metric include sensitivity to inflation caused by extensive self-citation within a journal and by single, highly-cited articles. For more information about the IF, see the essays of Dr. Eugene Garfield, founder of ISI. Determining a journal's IF requires access to Thomson Reuters Journal Citation Reports, not available at IC Library.
The eigenfactor is a more recent, and freely-available metric, devised at the University of Washington by Jevin West and Carl Bergstrom.3 Where the IF counts all citations to a given article as being equal, the eigenfactor weights citations based on the impact of the citing journal. Its creators assert that it can be viewed as "a rough estimate of how often a journal will be used by scholars." Eigenfactor values are freely avialable at eigenfactor.org.
SCImago Journal Rank Indicator
The SCImago Journal Rank indicator (SJR) is another open-source metric.4 It uses an algorithm similar to Google's PageRank. Currently, this metric is only available for those journals covered in Elsevier's Scopus database. Values may be found at scimagojr.com.