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.

It assesses the extent to which Universities are able to provide students with meaningful access to lecturers and tutors, and recognises that a high number of faculty members per student will reduce the teaching burden on each individual academic.

There is not a golden rule for the SSR, because SSRs vary according to course requirements and the nature of teaching within certain subjects.

Where do SSRs appear?

League tables

A number of national and international league tables use SSR, with slight differences in methods and weights attributed to this metric.

In The Guardian University Guide, SSR constitutes 15% of the University overall score and a low SSR is treated positively.

The Times Higher Education World University Rankings uses the inverse of the SSR, hence Staff/Student Ratio, where it constitutes 4.5% of the University overall score and a high Staff/Student Ratio is treated positively.

The QS World University Rankings uses the inverse of the SSR, hence Staff/Student Ratio, where it constitutes 20% of the University overall score and a high Staff/Student Ratio is treated positively.

Planning

SSR can be used as a proxy for a teaching unit's operational capacity, thus informing planning activities regarding staff, student and facilities.

Why there are different SSRs for the same department?

Although it only takes two numbers as inputs, there is a wide variety of methods to select which students and staff members will be included in the calculation. A list of factors that affect the selection of the population follows below:

Staff:

Type of contract (academic, non-academic)
Academic function (teaching only, teaching and research, research only)
Terms of employment (typical, atypical contracts)

Students:

Type of record (“in league tables” students - i.e. students whose records are submitted to HESA, all students)
Location of study (industrial placement, abroad, franchised)
Status (will a Post Graduate Research student in the write up stage be included or not?)

Level of aggregation:

Students (available at programme, department, subject, and cost centre levels)
Staff (figures are readily available at cost centre level only)
Multi-department programmes (will students count towards the route owner only or will they be split into the respective departments?)

SSR in The Guardian

The Guardian uses data from the SSR data set provided by HESA.

Students are selected based on type of record and the FTE is weighted by location of study. Staff are selected by type of contract, academic function and terms of employment.

The figures are aggregated at cost centre level.

The method is thoroughly explained on https://www.hesa.ac.uk/support/definitions/technical#studentstaff-ratios-ssr or on https://www.theguardian.com/education/2019/jun/07/methodology-behind-the-guardian-university-guide-2020

Because a cost centre may encompass multiple subject areas, there is no perfect alignment among departments and cost centres.

SSR in RAMP

SSR can be calculated for planning purposes using RAMP figures. A common approach is to use student figures from RAMP Students Report and staff figures from RAMP Staff Analysis.

Students are selected based on their type of record and status. Staff are selected by type of contract, academic function and terms of employment.

Student figures can be aggregated at department or route level, whereas staff figures are aggregated at department level.

Students from multi-department programmes that are aggregated at department level are not apportioned as per the teaching load split. Therefore, this method might inflate the amount of students in departments with many multi-department programmes.

Since cost centres and Departments do not align, this internal SSR is not perfectly in sync with The Guardian's metric, despite possibly being closer to the actual figure.

SSR in the UG Degree Outcomes - Extended Dataset

The UG Degree Outcomes - Extended Dataset workbook calculates SSR using rounded staff data from HESA and student data from SITS.

Students are selected based on their type of record and status. Staff are selected by type of contract, academic function and terms of employment.

Student figures are aggregated at department level, based on route ownership only, whereas staff figures are aggregated at cost centre level.

Since cost centres and Departments do not align, this internal SSR is not perfectly in sync with The Guardian's metric, despite possibly being closer to the actual figure.