Success rates for EPSRC proposals

December 12, 2013
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(This article was first published on James Keirstead » Rstats, and kindly contributed to R-bloggers)

In my last post, I looked at the success rates for EPSRC Fellowship applications using funnel plots. As luck would have it, Alex Hulkes and Derek Gillespie from EPSRC then got it touch to say that they had done a similar internal analysis and would I be interested in the data? Yes please!

The new data set considers EPSRC research grants as a whole, and gives the number of applications and success rates for 137 UK universities for four years, 2009-10 to 2012-13. I wanted to add a bit of colour to this data, literally and figuratively, and so I added a column to indicate whether the university was member of the research-intensive Russell Group or the (now defunct) 1994 Group. The basic funnel plot is shown below; note that I’ve normalised the results for each year to allow direct comparison.

Funnel plot of EPSRC proposal success rates

Funnel plot of EPSRC proposal success rates

Unfortunately with so many institutions, it’s a bit tricky to make out individual universities. Here I’ve highlighted an example university from each of the three groups. It would be fun to make an interactive version of this using D3 but since it took me ages to make these maps, I think I might pass on this for the moment.

Funnel plot of EPSRC proposal success rates, highlighting three example universities

Funnel plot of EPSRC proposal success rates, highlighting three example universities

Another question is how the performance of each university has changed over time; I’ve focused on the Russell Group universities to keep things legible. The first question is how to measure “performance”. I’ve done this by calculating, for each university, what is the probability of observing the actual number of successful applications given their total number of submitted proposals and the overall average success rate for that year. In R, this corresponds to the pbinom function and the result gives a score between 0 and 1. I’ve scaled this between 0 (bad) to 100 (good).

To make the plots, I’m using a new and improved version of some code that I wrote earlier for generating Tufte-style slopegraphs. The revised code allows the user to choose different layout algorithms. So in the first plot, the order of the universities is determined only by their rank in 2009-2010; each group line is then constrained not to overlap. This makes it easy to see how a particular university has changed over time, but not their overall rank in subsequent years.

Slopegraph showing changes in success rates for Russell Group universities using Tufte-style layout

Slopegraph showing changes in success rates for Russell Group universities using Tufte-style layout

In the second version, the position is based on rank in each year.

Slopegraph showing changes in success rates for Russell Group universities using rank-based layout

Slopegraph showing changes in success rates for Russell Group universities using rank-based layout

We might ask which university is “best”. I’ve calculated this as the average corrected probability of success in each year (see above), weighted by the number of submitted proposals. The table below gives the top 10 universities within each group.


Group University Adjusted success rate Applications submitted
Russell Group University of Cambridge 0.934 442
University of Bristol 0.924 310
University of Oxford 0.907 437
Durham University 0.906 159
University College London 0.797 523
Newcastle University 0.786 196
University of Sheffield 0.775 352
University of Leeds 0.759 324
Cardiff University 0.742 182
Imperial College London 0.735 664
1994 Group Institute of Education, University of London 0.882 3
University of Lancaster 0.770 127
Goldsmiths, University of London 0.655 10
Royal Holloway, University of London 0.576 52
University of Leicester 0.574 82
Birkbeck, University of London 0.551 14
University of East Anglia 0.515 70
Loughborough University 0.453 251
University of Sussex 0.433 76
University of Essex 0.317 42
Other Institute of Development Studies 1.000 1
National Institute of Agricultural Botany 1.000 3
Queen Margaret University Edinburgh 1.000 1
Rothamsted Research 1.000 5
Scottish Agricultural College 1.000 1
University of the Highlands and Islands 1.000 1
NERC British Geological Survey 0.938 6
EURATOM/CCFE 0.908 2
Transport Research Laboratory Ltd 0.862 3
John Innes Centre 0.848 2

I also calculated the performance of the groups as a whole and the Russell Group comes out on top.


Group Adjusted success rate Average applications
submitted per university
Russell Group 0.676 289.7
1994 Group 0.533 72.7
Other 0.429 30.2

This last table made me wonder if there was any connection between the number of submitted applications and the adjusted success rate (i.e. taking into account the binomial model). In other words, by submitting more applications, are you changing the probability of success? This could be explored with a fancy multi-level model, but I’ve just gone for the basic scatter plot below. The short answer appears to ‘not really’. The more formal answer is a linear model has an insignificant fit with an adjusted r2 value of 0.011.

Scatter plot showing number of applications and binomial-adjusted success rates

Scatter plot showing number of applications and binomial-adjusted success rates

There are many more questions that a person could explore with this data, so kudos to EPSRC for making it available.

To leave a comment for the author, please follow the link and comment on his blog: James Keirstead » Rstats.

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