How do we classify institutions?
The QS World University Rankings attract a great deal of interest and scrutiny each year, one piece of frequent feedback is the comparing “apples with oranges” observation. The simple fact is that the London School of Economics bears little resemblance to Harvard University in terms of funding, scale, location, mission, output or virtually any other aspect one may be called upon to consider – so how is it valid to include them both in the same ranking. They do, however, both aim to teach students and produce research and it has always been the assertion of QS that this ought to provide a sufficient basis for comparison.
In essence, it is a little like comparing sportspeople from different disciplines in a “World’s greatest sportsperson” or “World’s greatest Olympian” ranking which so frequently emerge. How is it possible to compare a swimmer with a rower with a boxer with a football player? Yet such comparisons have fuelled passionate conversation all over the world. The difference, perhaps, is that in that context those talking are aware of who represents what sport. That is where the classifications come in – they are a component appearing in the tables from 2009 that help the user distinguish the boxers from footballers, so to speak.
The Berlin Principles (a set of recommendations for the delivery of university rankings) assert that any comparative exercise ought to take into account the different typologies of its subject institutions, whilst an aggregate list will continue to be produced it will now feature labels so that institutions (and their stakeholders) of different types can easily understand their performance not only overall but also with respect to institutions of a similar nature.
Based very loosely on the Carnegie Classification of Institutions of Higher Education in the US, but operated on a much simpler basis, these classifications take into account three key aspects of each university to assign their label.
Based on the (full time equivalent) size of the degree-seeking student body. Where an FTE number is not provided or available, one will be estimated based on common characteristics of other institutions in the country or region in question.
|XL||Extra Large||>= 30,000|
Four categories based on the institution’s provision of programs in the five broad faculty areas used in the university rankings. Due to radically different publication habits and patterns in medicine, an additional category is added based on whether the subject institution has a medical school.
|FC||Full Comprehensive||All 5 faculty areas + medical school|
|CO||Comprehensive||All 5 faculty areas|
|FO||Focused||> 2 faculty areas|
|SP||Specialist||<= 2 faculty areas|
Since 2011, five age bands based on supplied foundation years.
|5||Historic||>= 100 years old|
|4||Mature||< 100 years old|
|3||Established||< 50 years old|
|2||Young||< 25 years old|
|1||New||< 10 years old|
our levels of research activity evaluated based on the number of documents retrievable from Scopus in the five year period preceding the application of the classification. The thresholds required to reach the different levels are different dependent on the institutions pre-classification on aspects 1 and 2.
Since their introduction for the 2009 table the QS Classifications have met with mixed feedback – positive feedback for the concept and the supporting research but less positive feedback for the notation used. In the 2010 table we have implemented a dramatically simple and transparent notation introducing three columns – one for each of the above metrics.
The intention is not to infer a hierarchy – the ranking exists for that purpose – XL is not a fundamentally preferable classification to S, nor is it intrinsically preferable to be FC, but to qualify the subject institutions by broad type with a view to making ranking results more contextually relevant to their increasingly broad audience.
For clarity the Research Intensity above is simplified – clearly an smaller institutions ought to produce less research than a larger one.
|VH||SP||2 x mean for specialist areas||2 x mean for specialist areas||2 x mean for specialist areas||2 x mean for specialist areas|
|HI||SP||1 x mean for specialist areas||1 x mean for specialist areas||1 x mean for specialist areas||1 x mean for specialist areas|
|MD||SP||0.5 x mean for specialist areas||0.5 x mean for specialist areas||0.5 x mean for specialist areas||0.5 x mean for specialist areas|
For more information about Scopus data, click here.