What is an H-index and how is it calculated? from www.shutterstock.com.au
A previously obscure scholarly metric has became an item of heated public debate. When it was announced that Bjorn Lomborg, a researcher who is sceptical about the human causes of climate change, would be heading a research centre at the University of Western Australia, the main retort from most scientists was “just look at the guy’s H-index!”
Many scientists who were opposed to Lomborg’s new research centre pointed out that his H-index score was 3. Usually, someone appointed to a professorship in the natural sciences would be expected to have an H score about ten times that.
For people outside of academia this measure probably makes little sense. So what exactly is an H-index and why should we use it to judge whether someone should be appointed to lead a research centre?
What is the H-index and how is it calculated?
The H-Index is a numerical indicator of how productive and influential a researcher is. It was invented by Jorge Hirsch in 2005, a physicist at the University of California. Originally, Professor Hirsch wanted to create a numerical indication of the contribution a researcher has made to the field.
At the time, an established measure was raw citation counts. If you wanted to work out how influential a researcher was, you would simply add up the number of times other research papers had cited papers written by that researcher.
Although this was relatively straightforward, researchers quickly discovered a significant problem with this score – you could get a huge citation count through being the scientific equivalent of a one-hit wonder.
If you published one paper that was widely cited and then never published a paper again after that, you would technically be successful. In such situations, outliers would have an undue and even distorting effect on our overall evaluation of a researcher’s contribution.
To rectify this problem, Hirsch suggested another approach for calculating the value of researchers, which he rather immodestly called the H-index (H for Hirsch of course). This is how he explains it:
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.
To put it in a slightly more simple way – you give an H-index to someone on the basis of the number of papers (H) that have been cited at least H times. For instance, according to Google Scholar, I have an H-index of 28. This is because I have 28 papers that are cited at least 28 times by other research papers. What this means is that a scientist is rewarded for having a range of papers with good levels of citations rather than one or two outliers with very high citations.
It also means that if I want to increase my H-index, it is best to focus on encouraging people to read and cite my papers with more modest citation levels – rather than having them focus on one or two well-known papers which are already widely cited.
The influence of the H-index
While the H-index might have been created for the purpose of evaluating researchers in the area of theoretical physics, its influence has spread much further. The index is routinely used by researchers in a wide range of disciplines to evaluate both themselves and others within their field.
For instance, H-indexes are now a common part of the process of evaluating job applicants for academic positions. They are also used to evaluate applicants for research grants. Some scholars even use them as a sign of self-worth.
Calculating a scholar’s H-index has some distinct advantages. It gives some degree of transparency about the influence they have in the field. This makes it easy for non-experts to evaluate a researcher’s contribution to the field.
If I was sitting on an interview panel in a field that I know nothing about (like theoretical physics), I would find it very difficult to judge the quality of their research. With an H-index, I am given a number that can be used to judge how influential or otherwise the person we are interviewing actually is.
This also has the advantage of taking out many of the idiosyncratic judgements that often cloud our perception of a researcher’s relative merits. If for instance I prefer “salt water” economics to “fresh water” economics, then I am most likely to be positively disposed to hiring the salt water economist and coming up with any argument possible to not accept the fresh water economist.
If however, we are simply given an H-index, then it it becomes possible to assess each scholar in a slightly more objective fashion.
The problems with the H-index
There are some dangers that come with the increasing prevalence of H-scores. It is difficult to compare H-scores across fields. H-scores can often be higher in one field (such economics) than another field (such as literary criticism).
Like any citation metric, H-scores are open to manipulation through practices like self-citation and what one of my old colleagues liked to call “citation soviets” (small circles of people who routinely cite each other’s work).
The H-index also strips out any information about author order. The result is that there is little information about whether you published an article in a top journal on your own or whether you were one member of a huge team.
But perhaps the most worrying thing about the rise of H-scores, or any other measure of research productivity or influence for that matter, is they actually strip out the ideas. They allow us to talk about intellectual endeavour without any reference at all to the actual content.
This can create a very strange academic culture where it is quite possible to discuss academic matters for hours without once mentioning an idea. I have been to meetings where people are perfectly comfortable chatting about the ins and outs of research metrics at great length. But little discussion is had about the actual content of a research project.
As this attitude to research becomes more common, aspirational academics will start to see themselves as H-index entrepreneurs. When this happens, universities will cease to be knowledge creators and instead become metric maximisers.