PS. For my further thoughts on tuition fees in UK universities, see here.
Ramblings on academic-related matters. For information on my research see http://psyweb.psy.ox.ac.uk/oscci/. Twin analysis blog: http://dbtemp.blogspot.com/ . ERP time-frequency analysis blog: bishoptechbits.blogspot.com/ . For tweets, follow @deevybee.
Correlations between Nstaff and QR funding are very high –above .9. Nevertheless, this analysis shows that, as is evident in Table 1, if we substituted size-related funding for QR funding, the amounts gained or lost by individual departments can be substantial. In some subjects, though, mainly in the Humanities, where overall QR allocations are anyhow quite modest, the difference between size-related and QR funding is not large in absolute terms. In such cases, it might be rational to allocate funds solely by Nstaff and ignore quality ratings. The advantage would be an enormous saving in time – one could bypass the RAE or REF entirely. This might be a reasonable option if the amount of expenditure on the RAE/REF by the department exceeds any potential gain from inclusion of quality ratings.
Is the departmental H-index useful?
If we assume that the goal is to have a system that approximates the outcomes of the RAE (and I’ll come back to that later) then for most subjects you need something more than Nstaff. The issue then is whether an easily computed department-based metric such as the H-index or total citations could add further predictive power. I looked at the figures for two subjects where I had computed the departmental H-index: Psychology and Physics. As it happens, Physics is an extreme case: the correlation between Nstaff and QR funding was .994. Adding an H-index does not improve prediction because there is virtually no variance left to explain. As can be seen from Table 1, Physics is a case where use of size-related funding might be justified, given that the difference between size-related and QR funding averages out at only 8%.
For Psychology, adding the H-index to the regression explains a small but significant 6.2% of additional variance, with the correlation increasing to .95.
But how much difference would it make in practice if we were to use these readily available measures to award funding instead of the RAE formula? The answer is more than you might think, and this is because the range in award size is so very large that even a small departure from perfect prediction can translate into a lot of money.
Table 2 shows the different levels of funding that departments would accrue depending on how the funding formula is computed. The full table is too large and complex to show here, so I'll just show every 8th institution. As well as comparing alternative size-related and H-index-based (QRH) metrics with the RAE funding formula (QR0137), I have looked at how things change if the funding formula is tweaked: either to give more linear weighting to the different star categories (QR1234), or to give more extreme reward for the highest 4* category (QR0039) – something which is rumoured to be a preferred method for REF2014. In addition, I have devised a metric that has some parallels with the RAE metric, based on the residual of the H-index after removing effect of departmental size. This could be used as an index of quality that is independent of size; it correlates with r = .87 with the RAE average quality rating. To get an alternative QR estimate, it was substituted for the average quality rating in the funding formula to give the Size.Hres measure.
Table 2: Funding results in £K from different metrics for seven Psychology departments representing different levels of QR funding
To avoid invidious comparisons, I have not labelled the departments, though anyone who is curious about their identity could discover them quite readily. The two columns that use the H-index tend to give similar results, and are closer to a QR funding based that treats the four star ratings as equal points on a scale (QR1234). It is also apparent that a move to QR0039 (where most reward is given for 4* research and none for 1* or 2*) will increase the share of funds to those institutions who are already doing well, and decrease it for those who already have poorer income under the current system. One can also see that some of the Universities at the lower end of the table – all of them post 1992 universities – seem disadvantaged by the RAE metric, in that the funding they received seems low relative to both their size and the H-index.
The quest for a fair solution
So what is a fair solution? Here, of course, lies the problem. There is no gold standard. There has been a lot of discussion about whether we should use metrics, but much less discussion of what we are hoping to achieve with a funding allocation.
How about the idea that we could allocate funds simply on the basis of the number of research-active staff? In a straw poll I’ve taken, two concerns are paramount.
First, there is a widely held view that we should give maximum rewards to those with highest quality research, because this will help them maintain their high standing, and incentivise others to do well. This is coupled with a view that we should not be rewarding those who don’t perform. But how extreme do we want this concentration of funding to be? I’ve expressed concerns before that too much concentration in a few elite institutions is not good for UK academia, and that we should be thinking about helping middle-ranking institution become elite, rather than focusing all our attention on those who have already achieved that status. The calculations from RAE in Table 2 show how a tweaking of the funding formula to give higher weighting to 4* research will take money from the poorer institutions and give it to the richer ones: it would be good to see some discussion of the rationale for this approach.
The second source of worry is the potential for gaming. What is to stop a department from entering all their staff, or boosting numbers by taking on extra staff? The first point could be dealt with by having objective criteria for inclusion, such as some minimal number of first- or last-authored publications in the reporting period. The second strategy would be a risky one, since the institution would have to provide salaries and facilities for the additional staff, and this would only be cost-effective if the QR allocation would cover it. Of course, a really cynical gaming strategy would be to hire people briefly for the REF and then fire them once it is over. However, if funding were simply a function of number of research-active staff, it would be easy to do an assessment annually, to deter such short-term strategies.
How about the departmental H-index? I have shown that it not only is a fairly good predictor of RAE QR funding outcomes on its own, incorporating as it does both aspects of departmental size and research quality, but it also correlates with the RAE measure of quality, once the effect of departmental size is adjusted for. This is all the more impressive when one notes that the departmental H-index is based on any articles listed as coming from the departmental address, whereas the quality rating is based just on those articles submitted to the RAE.
There are well-rehearsed objections to the use of citation metrics such as the H-index: first any citation-based measure is useless for very recent articles. Second, citations vary from discipline to discipline, and in my own subject, Psychology, within sub-disciplines.. Furthermore, the H-index can be gamed to some extent by self-citation, or scientific cliques, and one way of boosting it is to insist on having your name on any publication you are remotely connected with - though the latter strategy is more likely to work for the H-index of the individual than for the H-index of the department. It is easy to find anecdotal instances of poor articles that are highly cited and good articles that are neglected. Nevertheless, it may be a ‘good enough’ measure when used in aggregate: not to judge individuals but to gauge the scientific influence of work coming from a given department over a period of a few years.
The quest for a perfect measure of quality
I doubt that either of these ‘quick and dirty’ indices will be adopted for future funding allocations, because it’s clear that most academics hate the idea of anything so simple. One message frequently voiced at the Sussex meeting was that quality is far too complex to be reduced to a single number. While I agree with that sentiment, I am concerned that in our attempts to get a perfect assessment method, we are developing systems that are ever more complex and time-consuming. The initial rationale for the RAE was that we needed a fair and transparent means of allocating funding after the 1992 shake-up of the system created many new universities. Over the years, there has been mission creep, and the purpose of the RAE has been taken over by the idea that we can and should measure quality, feeding an obsession with league tables and competition. My quest for something simpler is not because I think quality is simple, but rather because I think we should use the REF just as a means to allocate funds. If that is our goal, we should not reject simple metrics just because we find them oversimplistic: we should base our decisions on evidence and go for whatever achieves an acceptable outcome at reasonable cost. If a citation-based metric can do that job, then we should consider using it unless we can demonstrate that something else works better.
I'd be very grateful for comments and corrections.