Student : Staff Ratio (SSR) Methodology Consultation 22

25 August 2022      Jen Summerton, Executive Director

Download full supporting information, including appendices, as a document.

View and respond to the consultation.

For the past several years, HESA has been producing data to support calculation of Student : Staff Ratios (SSRs) for use by league table compilers and anyone else interested in comparing resourcing levels across the sector. The last review of the methodology used for this took place in 2015, so it is time to revisit this calculation to ensure it remains fit for purpose.

Since the last review, a lot of things have changed: HESA has become the Designated Data Body for English Higher Education and has transferred a number of its former roles to Jisc, as part of ongoing joint working/merger discussions. As a result, Jisc now administers the supply of data for these purposes, and HESA has determined that it is no longer appropriate for it to ‘own’ the SSR methodology and Jisc’s role is in administering and sharing the data, rather than leading on these sorts of definitions.

As an active partner in the last SSR methodology review, the Higher Education Strategic Planners Association (HESPA), through its HE Data Insight Group (HEDIG), has agreed to take up the mantle of custodian of this methodology. We have done so because we firmly believe that there is value in shared, sector-owned, and transparent methodologies for this sort of metric. While no one we spoke to during the first phase of this review expressed unambiguous support for the concept of SSRs, there is a clear consensus that moving away from a single reproducible methodology to multiple variants (such as a different approach for each league table) is undesirable

A working group made up of representatives from HESA, Jisc and a range of different types of higher education providers (see appendix 1 for full details) has undertaken an in-depth review of the current methodology (see appendix 2 for current methodology). The review explored opportunities to improve the methodology, investigating the following options:

  • Develop an entirely different approach that would fulfil the same purposes
  • Revise the current approach by changing the calculations applied to the staff data
  • Revise the current approach by changing the calculations applied to the student data
  • Other broader changes to methodology.

Potential improvements were categorised according to those that could be implemented with no change to data collection requirements and those that would require such changes. The former were prioritised for this review, with the remainder being noted for input to consultations on future collection changes.

Options explored were:

  1. Creation of an entirely different metric to replace SSRs: SSRs are generally used as a proxy for class size or contact time with academic staff, so potential alternative metrics which might better fulfil such purposes were explored.

Options considered included use of attendance data or timetable data, but these would require additional data collection.

It had at one time appeared that Key Information Set (KIS) data might develop into an appropriate source for this type of use, but revisions to this collection have removed the level of detail that would be required.

Another option considered was to calculate teaching staff cost per student FTE, however this could be misleading because high scores might be indicative of high numbers of staff, but they might equally represent smaller numbers of highly paid staff.

The review group concluded that, at this time, no suitable alternative metrics were readily available.

  1. Amendments to the staff FTE calculation: The following options were considered as potential improvements to the accuracy of the staff data used in SSRs:
  2. Reducing the FTE of staff classified as having responsibilities for both teaching and research to account for the fact that these staff will not spend 100% of their time on student-related activities. Staff classified as teaching only would remain as 100% and those classified as research only as 0%. There is a range of academic workload models in use across the sector so the proportion of time spent away from student-related activities will vary between institutions, but it was felt that some reduction was appropriate.
  3. Alternatives to a blanket FTE reduction were also examined:
  • Data on sources of basic salary were examined in detail to determine their suitability for use in identifying the teaching element of staff time. The review group found that only 1% of staff had multiple sources of basic salary, meaning that the impact of such a change would be minimal. In addition, for effective use of this field, additional data relating to the proportion of salary funded by each source would also be required, so the staff collection would need to be expanded. The review group therefore concluded that this could not be recommended at this time.
  • The relationship between the academic staff FTE and research funding was examined to identify whether a formula could be created to reduce staff FTE to account for research activity. Whilst a relationship was detected, it was concluded that it was not possible to quantify this in a sufficiently robust manner.
  1. Amendments to the student FTE calculation: The following options were considered as potential improvements to the accuracy of the student data used in SSRs to account for the fact that a number of students spend a significant proportion of their time off campus learning in workplace settings and being taught by professionals in those settings rather than academic staff. As these professionals are not employed by the HEP, they are not included in the staff side of the calculation, so this is not offset in that way. Course lengths vary, but will typically be over 40 weeks pa for these types of courses leading to professional registration, and placements can account for up to 70% of time on some teacher training courses. A reduction in FTE for these students would complement the approach to reducing the FTE of students on full-year industrial placements by 50%. The following data were considered for this purpose:
    1. Location of study field
    2. Industrial placement data
    3. Professional body indicators.

Option c was identified as being the most useful for this purpose and it was agreed that some reduction would be appropriate.

  1. Other broader changes to methodology: cost centre coding is currently the only subject-related classification that is common to all of the student, staff and finance HESA records, but increasing use is being made of the new approach to subject coding through HECoS and the common aggregation hierarchy (see The review group considered whether there would be benefits to changing SSRs to use HECoS.

After careful consideration, the group concluded that this would not improve the accuracy of SSRs because it would be likely to create as many problems as it would solve. For example, there are challenges involved in assigning HECoS codes to academic staff: their HECoS is determined by their discipline. This is fine if every course is taught entirely by people whose discipline matches that of the subject of the course but that is not always the case. In addition, many courses allow students to take one or more optional modules outside their specialism. It would also require more robust collection of HECoS data within the three records and would therefore take time to implement.


Having explored all the above options, the review group is seeking views on the following proposed changes:

  1. A reduction of the FTE of staff with responsibilities for research.

         Due to variations in workload models across the sector, we are interested to receive views on whether this           reduction should be 20%, 30% or 40%.

  1. A reduction of the FTE of students on professional courses involving substantial time in workplace settings.

         Variations in course lengths and time spent in such settings mean that we are interested in hearing views              on whether such a reduction should be 20%, 30% or 40%.

How to respond:

The deadline for responses to this consultation is 4 November 2022.

Respond to the consultation here.

Next steps

Once the consultation period has closed, all responses will be collated and considered by the review group and a final position will be agreed.

Technical documentation will be developed and this will be passed to Jisc for use in data dissemination as well as being hosted in an open and accessible location on the HESPA website for all interested parties to download and use.

Once adopted, all future requests for SSRs will use the new methodology and this will be applied retrospectively to previous years of data to provide a time line for trend analysis.

The definition will be reviewed every five years.

Appendix A: Review Group membership

Jonathan Waller, HESA

Dee Jones, Jisc

Jackie Njoroge, University of Salford

Jackie Groves, Cardiff University

Jenny Walker, Loughborough University

Kirsty Roden, Glasgow Caledonian University

Dave Radcliffe, University of Birmingham

Amy Whitmore, De Montfort University

Luan Heggarty, MMU

Jen Summerton, HESPA

Sally Turnbull, University of Wolverhampton and HEDIG Chair

Appendix B: Student Staff Ratios – current methodology

This information is taken from the HESA website:

The student: staff ratio (SSR) is designed to show the total number of students per member of academic teaching staff. The SSR is calculated using the student and staff full-time equivalent (FTE).

PLEASE NOTE: The mobility data (comprising LOCSDY codes T and U, and the mobility types) that we use for two of the FTE reductions listed below has only been in the HESA Student record since 2013/14, and we cannot provide comparable data for any year prior to this.

Student component (numerator)

The student figures are based on the HESA session population, and include students counted as 1 (HE session population) and 2 (FE session population). We split the student data into 2 portions, one for those students for whom we include the full FTE (the ‘basic FTE’) and the other for those for whom we make a reduction (the ‘reduced FTE’).

A reduction is made to the FTE for the following students:

  • Students with a location of study of D “On industrial (or other) placement for the year as a whole” – FTE reduced to 20% of the original.
  • Students with a location of study of E “On industrial (or other) placement for a proportion of the year” – FTE reduced to 60% of the original.
  • Students with a location of study of T “Abroad for the whole year” who are not studying for any part of their time abroad (e.g. none of their mobility types are 01 Study abroad) – FTE reduced to 20% of the original.
  • Students with a location of study of U “Abroad for a proportion of the year” who are not studying for any part of their time abroad (e.g. none of their mobility types are 01 Study abroad) – FTE reduced to 60% of the original.
  • Students who are studying under a franchise arrangement – we only include the proportion of the FTE that is taught by the reporting HE provider (e.g. if a student is 30% franchised, we would include 70% of their FTE).

NOTE: Where the student falls into one of the LOCSDY (locations of study) categories above and is being taught under a franchise arrangement, then both reductions will be applied (e.g. if a student has a location of study of D and is 50% franchised, then we would only include 10% of their FTE).

Staff component (denominator)

The staff figures include academic 'teaching only’ and ‘teaching and research’ staff in the HESA Staff contract session population (i.e. F_ACEMPFUN is 1 or 3, and F_XPSESC01=1). In some analysis, atypical staff (identified in the terms of employment field – F_TERMS=3) may be shown separately.

Calculation of SSR

Once the 4 constituent parts have been generated, the SSR is calculated by summing together the student components and dividing them by the summed staff components. As standard, atypical staff are included in HESA SSR calculations but may be omitted if required.


To avoid SSRs being calculated on low numbers, data is suppressed if the student component is less than or equal to 7 FTE or if the staff component is less than or equal to 2 FTE.

SQL code to aid re-creation of SSRs

Fields used are from HESA records, and include some in-house derived fields which are not passed on to HE providers. The SSR_SQL document includes the full technical specifications for these fields.

Download full supporting information, including appendices, as a document.

View and respond to the consultation.

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