The Student/Staff Ratio (SSR) compares the number of staff teaching a subject with the number of students studying it. It is a measure of human resource input that is deemed to affect the student experience and, ultimately, the academic attainment.
SSRs vary according to course requirements and the nature of teaching within certain subjects. Therefore, it is not advisable to perform cross-subject comparisons.
Where SSR appears?
League tables - Where a low SSR is treated positively in the league tables
Planning - Where it is used as a proxy for a teaching unit's operational capacity
Why there are many different SSRs for the same department?
Although it only takes two numbers as inputs, SSRs can be calculated using a wide variety of student or staff numbers.
When it comes to staff numbers, one must decide whether to include academic and non-academic staff, and whether to include research only academic staff or not.
As for student numbers, one must decide whether to use the net number of students or just the number of students reported to HESA. Also, a decision must be made regarding the levels of study to be included. Finally, will a Post Graduate Research student in the write up stage be included or not?
On top of these considerations, there must be a definition about the grouping level. Student figures are available at programme, department, subject, and cost centre levels, whereas staff figures are readily available at cost centre level only.
SSR on the Department Degree Results Explorer
Student/staff ratio shows the total number of students reported to HESA at all levels of study (FTE) per member of staff (FTE).
This metric uses staff data sourced from HESA, therefore subject to HESA rounding and suppression rules. The student numbers stem from internal data sets.
The data set covers both academic and non-academic staff, where academic is defined as those whose academic employment function is not defined as "Research only".
The data set covers only staff in a typical working contract. Atypical contracts are contracts that involve non-permanent working arrangements or complex employment relationships.
The SSR on the workbook has been updated so as to calculate the SSR using only “in league tables” students, i.e. students whose records are submitted to HESA.
The difference stems from the fact that The Guardian uses data from the “HESA Student Staff Ratios by Cost Centre and provider (SSRs) v1” dataset, where they obtain SSR by Cost centre, whereas we use student numbers by department divided by staff FTE by cost centre.
Since Cost centres and JACS codes or Departments do not align, the internal SSR is not a perfect metric, despite being closer to the actual figure.
SSR on RAMP
RAMP reports calculate SSR using student population either by route owner or by department. The route owner numbers are much closer to the HESA figures.
There are some data quality issues with the salary forecaster, which underlies the staff reports on RAMP. In effect you are using two inflated figures to calculate the SSR, when compared to HESA or the workbook I mentioned.
SSR on The Guardian
The Guardian uses data from the “HESA Student Staff Ratios by Cost Centre and provider (SSRs) v1” dataset, where the ratio is presented by Cost centre.
Because the Cost centre 121 encompasses other subjects apart from computer science, the numbers are matched by JACS codes, where one finds a SSR of 14.6 (730 students FTE for subject area 8 – Computer science / 50 staff FTE for cost centre 121).
Non-academic staff or research only staff are not comprised in the staff FTE figure above.
It is unclear whether the SSR reported by The Guardian represents in fact the method explained on https://www.theguardian.com/education/2019/jun/07/methodology-behind-the-guardian-university-guide-2020