PIs 2005/06: Definitions (applicable to tables T1 to T2)
Definitions (applicable to tables T1 to T2)
Coverage
Higher education (HE) students are those students on programmes of study for which the level of instruction is above that of level 3 of the National Qualifications Framework, i.e. courses leading to the Advanced Level of the General Certificate of Education (GCE A-levels), the Advanced Level of the Vocational Certificate of Education (VCE A-levels) or the Advanced Higher Grade and Higher Grade of the Scottish Qualifications Authority (SQA) Advanced Highers/Highers).
The data used in constructing the indicators have been taken from the HESA database. The HESA Student Record contains information about individual enrolments, which, because a student can be enrolled on more than one programme of study, will exceed the number of students. Postdoctoral students are not included in the HESA Student Record.
All students included in the tables are those whose normal residence is in the United Kingdom, excluding the Channel Isles and the Isle of Man. This information comes primarily from the HESA POSTCODE field, with the DOMICILE field used if there is no valid postcode supplied. If neither field supplies valid information, it is assumed that the student is resident in the UK. Incoming and visiting exchange students and students studying for the whole of their programme of study outside the UK are excluded from the tables.
Age
Many of the tables are split between young and mature students, defined as follows:
- Young students are those who are aged under 21 at 30 September of the academic year in which they are recorded as entering the institution. So for students recorded as entering an institution in 2005/06, young students are those born after 30 September 1984.
- Mature students are those who are aged 21 or over, also at 30 September of the academic year in which they are recorded as entering the institution.
Students whose date of birth is not given, or whose date of birth suggests that they are under 10 years, are allocated to age group ‘unknown’. For tables which provide information about young students, mature students, and all students, this means that the numbers under ‘All students’ are not necessarily the sum of ‘Young students’ and ‘Mature students’.
Mode of study
- Full-time students are those recorded as studying full-time at an institution, or on thick or thin sandwich courses, provided that the length of the course is at least 24 weeks.
- Part-time students are those recorded as studying part-time, or full-time on courses lasting less than 24 weeks.
Level of study
The level of study is taken from the qualification aim of the student. Only undergraduate students are included in Tables T1 to T2 at present. First degree students are those studying for any type of first degree; other undergraduate students are those studying for foundation degrees, diplomas, certificates and other undergraduate courses. The codes for qualification aims (HESA field QUALAIM) used to define the level are shown below.
| 2005/06 QUALAIM codes | |
| First degree | 18, 20, 21, 22, 23, 24 |
| Other undergraduate | 15, 25, 26, 27, 28, 29, 30, 32, 33, 41, 42, 43, 44, 45, 51, 52, 61, 97 |
Entrants
Tables T1 to T2 provide information about entrants to an institution. These are defined as students who started a programme of study at that institution during the academic year of interest. This is based on the commencement date of the student’s study (HESA field COMDATE). While most entrants go into the first year of a programme of study, some will start on the second, or later, year of programme, for example if they transfer from another institution. Entrants who are recorded as leaving before 1 December (HESA field DATELEFT) have not been included in the calculations, unless the record contains important information such as a qualification. It has been agreed that students leaving this early in their studies should be disregarded for the purposes of the performance indicators.
Type of school
School type is taken from previous institution attended (HESA field PREVINST). All schools or colleges that are not denoted ‘independent’ are assumed to be state schools. This means that students from sixth-form or further education colleges, for example, are included as being from state schools.
Socio-economic classification
The information on socio-economic classification is taken from the National Statistics Socio-Economic Classification (NS-SEC). The classifications used are:
1 Higher managerial and professional occupations
2 Lower managerial and professional occupations
3 Intermediate occupations
4 Small employers and own account workers
5 Lower supervisory and technical occupations
6 Semi-routine occupations
7 Routine occupations
The performance indicator is the proportion of students from NS-SEC classes 4 to 7 (HESA field SEC codes 4, 5, 6 and 7) out of those from NS-SEC classes 1 to 7. NS-SEC class 8, long-term unemployed or never worked, has been included with unknown classification for the purposes of the performance indicators.
Low-participation neighbourhoods
This definition uses work carried out into the rates of participation in higher education of young people. Full details are provided below. Areas for which the participation rate is less than two-thirds of the UK average rate have been defined as low-participation neighbourhoods. Students have been allocated to these neighbourhoods on the basis of their postcodes, using the Super Profiles classification.
Geodemographic analysis and location-adjusted benchmarks – technical details
Defining areas
Any geodemographic analysis must start by defining the areas to be used. The starting point is generally a set of small administrative areas for which information is readily available. There is a range of classifications which can then be used to combine these small areas into groups. The classifier used here is the Super Profiles system.
The small areas taken are the Census enumeration districts (EDs) in England and Wales, and the output areas (OAs) in Scotland. The classification is based on data collected in the 1991 Census of Population, supplemented with data from other sources. Areas belonging to the same group, or cluster, will not necessarily be geographically adjacent. For example, one cluster might contain suburban areas of semi-detached housing from Leeds, Manchester, Birmingham and Bristol, and another may contain inner city areas from those same cities. Postcodes can be used to identify the ED (or OA) and hence the clusters. This ‘postcode mapping’ allows nearly all students to be allocated to one of the clusters, on the basis of their home postcode.
A small proportion of postcodes cannot be mapped to EDs, either because they have been wrongly recorded or because they are too new to have been included in the postcode file. Similarly, a small proportion of EDs have not been classified, for example if the number of residential dwellings in the area is too small to provide reliable information. In either case the result will be a cluster whose neighbourhood type is unknown. The 160 clusters which resulted from this method were classified as ‘low participation neighbourhood’ or ‘other neighbourhood’ by estimating, for each cluster, the participation rates in higher education for young entrants. These rates vary from under 5% to over 95%. Clusters with participation rates less than two-thirds of the national average were defined as ‘low participation’.
Population estimates
The participation rates as defined above depend on two elements: the population of the area and the number of students from that area. The number of students is taken from the HESA database, with postcodes used to allocate students to areas. The population estimates are an uncorrected projection of the 1991 Census population figures. HEFCE is working to create more accurate estimates of populations as part of a project to monitor participation across the sector, and these new estimates will be used to check the classification of neighbourhood types as ‘low participation’.
Localised effects
Under certain conditions the location of an institution can have an impact on the low participation neighbourhood indicator, making it appear different from the other widening participation indicators. In particular, there are three characteristics which have an impact on institutions in London:
Although most clusters are geographically widespread, some are concentrated in London. This is due to the special patterns of car ownership, methods of commuting, accommodation types and so on.
Institutions in London tend to recruit a high proportion of students from London.
The participation rate overall is higher in London than for most other parts of the country.
These factors taken together mean that areas in London may be less likely than similar areas elsewhere to be classed as low participation. As a result, institutions in London tend to have a lower proportion of students from low participation neighbourhoods relative to their benchmarks.
There are also other local effects which could have an impact on the rates of participation. For example, enumeration districts in some rural areas cover a greater area than those elsewhere, and so tend to include a wider range of household types. This could, in principle, lead to pockets of low participating groups being incorporated in high participation neighbourhood types. However, we have found no evidence that such effects have a significant impact on the statistics for institutions.
Measuring effects of locality
Supplementary Table SP1 shows the percentages of young entrants from each of the regions of the UK who come from low participation neighbourhoods; NS-SEC Classes 4, 5, 6 and 7; and state schools. The scale of the differences between regions means that institutions which recruit most of their students locally may find they have characteristics quite different from the national average.
Because of these differences, we have looked at ways in which a student’s domicile could be incorporated into the existing benchmarks of the widening participation indicators. Using the same methodology as is used for the current benchmarks, and taking the student’s region of origin as another factor, we have produced a value that will give an indication of how important the location factor is. This is the location-adjusted benchmark.
For institutions which recruit from across the UK, there is very little difference between the standard benchmark and the location-adjusted benchmark. Institutions which recruit more locally will have larger differences, possibly 3 or 4%, between the original and the location-adjusted benchmark. These larger differences show that the indicator is affected by the characteristic of the area the institution recruits from. In general, the greatest differences occur for the low participation indicator, and the smallest for the NS-SEC indicator.
Questions
In considering how best to measure locality effects, a major concern was raised. By allowing for the effects of locality, there is a danger that what we are trying to measure could be partly obscured. Differences between geographical areas may be caused by disparities between institutions, or these disparities may be the result of geographical differences. Until we have resolved this circularity we need to be careful in making allowances for geographical effects.
There is a further difficulty with the method used. In theory, if an institution situated in a region of low participation were to recruit predominantly from another region of high participation, that institution’s benchmark would not reflect its locality. Rather, it would reflect the locality from which its students were recruited. In practice that is unlikely to happen, partly because we have used region rather than some smaller geographical area as the basis.
The location-adjusted benchmark has only been used with the participation indicators, because of the known differences in the way these groups are spread across the country. They have not been used with the indicators of retention or non-continuation, nor is there any plan to do so, for two reasons. The major reason is that to include location as a factor in non-continuation would imply that people from different regions could have different continuation rates, even taking into account their subject of study and their entry qualifications. This would not be acceptable. A further reason is that the differences between the non-continuation rates for students from different regions is small. A location-adjusted benchmark for these indicators would therefore not provide any extra information.
Benchmarks
For definitions of the fields used to create the benchmarks, please refer to the benchmarks document. These fields include:
- subject of study
- entry qualifications
- region of domicile.
Context statistics
Two additional context statistics have been provided for the indicators in Tables T1, T2 and T3. These are:
- the average number of institutions in the adjusted sector benchmark comparison
- the average proportion which the institution’s own students contribute to the benchmark
These context statistics are provided for both the original benchmark and the location adjusted benchmark in Tables T1 and T2.
It is important to note that both of these statistics are average values. The numbers do not relate to specific institutions. The interpretation is fairly straightforward. If the average number of institutions in the comparison is small, say less than 20, then there are not many institutions whose students are similar to the one in question. If the students at the institution contribute a large proportion to the benchmark, say more than 20 per cent, then the adjusted sector benchmark will be similar to the institution’s own value. For the original benchmarks, very few institutions have a small number of comparable institutions or contribute a large proportion to the benchmark. For the location-adjusted benchmarks, the number of comparable institutions is likely to be smaller and the average contribution to the benchmark is likely to be larger than for the original benchmarks, and so the location-adjusted benchmarks are generally closer to the indicators than are the original benchmarks.
These statistics are designed, in particular, to pick up situations where the benchmark is of limited use because there are few other institutions that really are comparable.
Average number of institutions in comparison
The calculation of the two context statistics is based on the sector grid of entry
qualifications and subject of study (please refer to the benchmarks document for details). For each cell in the grid, we count the number of institutions with students in that cell. Let this number be nij for
subject i and entry qualification j. For the institution of interest, call the number of its students t,
and let tij be the number studying subject i with entry qualification j. Then for each cell compute 
, and sum these values over all cells. So the required value is:
average number of institutions in comparison 
Average contribution to benchmark
To find the contribution of the institution’s students to the benchmark, we use a similar
weighted average, but now of the proportion of each cell’s students who come from the
institution. If the number of students in the sector who are studying subject i and have entry
qualification j is Tij, then in any cell the institution’s students form a proportion 
of the total,
and the context statistic is the weighted average of these values, namely
average contribution to the benchmark 
Rounding strategy
Due to the provisions of the Data Protection Act 1998 and the Human Rights Act 1998, HESA implements a strategy in published and released tabulations designed to prevent the disclosure of personal information about any individual. This strategy involves rounding all numbers to the nearest 5. A summary of this strategy is as follows:
- 0, 1, 2 are rounded to 0
- All other numbers are rounded to the nearest 5.
So for example 3 is represented as 5, 22 is represented as 20, 3286 is represented as 3285, while 0, 20, 55, 3510 remain unchanged.
This rounding strategy is also applied to total figures; the consequence of which is that the sum of numbers in each row or column will rarely precisely match the total shown.
Average values, proportions and FTE values prepared by HESA will not be affected by the above strategy, and will be calculated on precise raw numbers. However, percentages and indicators calculated on populations which contain less than 20 individuals will be suppressed and represented as a blank value.
Enquiries
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