All data collected across the three modes (online, telephone and postal) are captured in a single location provided by the same software used to administer the survey. Every day, all completed survey results are transferred to HESA’s internal databases. These are processed overnight, ready for dissemination through the provider portal the following day.
Data captured in the internal databases are also used for quality assurance and output production.
A separate Data quality report is published on the website, detailing a current assessment of the strengths and weaknesses of the Graduate Outcomes data as well as providing information on any known quality issues. It also forms part of an advanced user’s guide to further information HESA has published on Graduate Outcomes, signposting technical specifications, papers, and reports of interest to analysts.
Where we have received sufficient data (more than one alpha-numeric character in one of the four employment fields) in the employment and/or self-employment sections of the survey, responses are passed on to Oblong, our supplier for coding of Standard Industrial Classifications (SIC) and Standard Occupational Classifications (SOC). Surveys completed in Welsh are first translated and then sent to the coding supplier.
The SOC2020 framework was used for coding the 2020/21 collection.
The fields used for SIC coding are:
Job Title (to help with School/Healthcare classifications)
Self-Employed or Own Business
The fields used for SOC coding are:
Job Duties Description
Most Important Activity
For business owners, whether they have employees
Whether they Supervise Others
The Company Description can help in some cases to clarify the SOC code. A combination of the Course Title studied and the Qualification Required question, where appropriate, help to inform and give confidence to the coding.
Ideally, all the above variables are needed to obtain the most relevant SOC code for a given record. In some instances, it may be possible to obtain a code even when all the information is not provided. However, as previously noted, at least one of the four employment fields must be provided as a minimum.
Over the years, our supplier has developed self-learning software to deal with the classification and matching of company data. This software has been re-written and trained to work with HESA data, and utilises fuzzy logic, knows of common typos and uses spelling error algorithms to deal with the free text in the data. The software is underpinned by our supplier’s own database of UK companies and uses machine learning on both SIC and SOC from historic data to improve coding. They also employ a dedicated team of manual coders who check all codes and fill gaps where the software could not apply a code.
Our supplier first loads the data into their systems and pre-processes it, tidying it up, addressing common issues and putting it into the right format ready for further automated processing. Each field has its own set of unique pre-processing tasks, which can range from keyword replacement, keyword removal and character substitution.
Next, industry classifications (SIC codes) are automatically added to companies that employ graduates. The manual coding team then complete an initial check of the data and fill the gaps where the system cannot apply a SIC code. The codes are then checked again by a quality control team and amended where necessary.
The data are then automatically SOC coded, and the system uses various methods to apply a SOC code to a record. It looks for keywords in both the job title and job duties fields. The system learns from data that have already been coded (including previous manually SOC coded records), so if it sees a record with similar details to one that was seen before, it can be assigned the same SOC code.
Of the responses collected through telephone interviewing, any uncodable records identified by Oblong are sent back to IFF for a follow-up interview where there is a reasonable case for going back to the respondents.
The SOC codes generated by the automated process are manually reviewed, and the gaps filled where the automated systems could not apply a code. All records are then sent to the SOC quality checking team to be checked before being released back to HESA.
The manual coders are in constant contact with each other and the quality team, and any new/different occupations encountered are discussed with the quality team, who will then research an occupation if necessary, or discuss with HESA or the ONS if required.
For most of the job titles, the coding index (list of job titles in the SOC framework) contain the job titles and records can be coded from them. Where the job title is not in the indexes detail in the job duties is used to ascertain what the job involves and code accordingly. Due to the international element of the data, jobs which do not appear in the indexes are also encountered. Coders are adept at assessing the job duties and placing the job with the appropriate code, and this is all subsequently checked by a quality checker. If a coder still cannot code then they raise a query with the quality checkers, who will discuss with other team members, research the role if necessary, and advise on coding. Research is done online using reputable sources (for example the company website where the person works, NHS websites, large well-known job sites, where one can see what qualifications are required and what a job involves). Where appropriate, the documentation which the coders use is subsequently amended for future reference.
Doing this exercise over multiple years, and given the volume of data, allows Oblong to refresh their databases with new jobs that did not exist before. When new jobs are encountered, a decision is made on an appropriate code and this information is disseminated to all coders via their coding indexes for future reference.
A final consistency check is completed at the end of each cohort and for many records a final data consistency/quality review takes place at the end of the collection. This involves consistency checks across employers, job titles and all cohorts to make sure no single cohort within the collection looks different to the rest. By the end of the process, every SOC and SIC code will have been manually checked multiple times.
With the introduction of SOC2020 for year two, our supplier has taken the opportunity to review all logic and associated reference data (over 50,000 sets of keywords and SOC associations) within the SOC coding automated systems, to ensure that provider feedback has been embedded in the software, and has also refined and added to the guidance documentation used to manually classify the responses. The manual coders have been retrained on the new SOC2020 taxonomy, and also continue to be re-briefed on an ongoing basis following changes based on quality assurance exercises and provider feedback.
Oblong also provide a standardised company name, improved business postcode, Companies House registration numbers and employee size information in the final, returned data, in order to aid analysis.
Following receipt of coded data, HESA undertook an extensive data quality assurance exercise. It was successfully completed in Spring 2023 and outcomes will be made available to users and other members of the public.
At the end of the collection process, data returned for questions that permit a free-text response goes through a cleansing process to improve data quality. This is usually where the respondent has not chosen a value from the drop-down list provided but has instead selected “other” and typed their own answer. Where appropriate, postcode information relating to location of employment and self-employment / running own business is mapped to a valid format to increase usability
From 2020/21, a drop-down list was introduced for the UK town, city or area of employment or self-employment / running own business to reduce the amount of free text cleansing required. If “other” is selected from the list and free text has been provided, where possible the free text is mapped to an appropriate value from the drop-down list or a region or country. A detailed account of this process is available in a later section.
Prior to the removal of free text boxes in 2019/20, information relating to home country, country of further study, employment and self-employment / running own business and salary currency was cleansed in a similar way. Prior to 2020/20 when the text box option was removed, information relating to UK provider of further study and previous study was also cleansed.
Further aggregation of some key fields is carried out to produce standard derived breakdowns used across HESA’s published material. Key areas of derivation include minimum response for inclusion in publication, method of response, activity (including most important activity), location of activity; grouping of standard industrial classification (SIC), standard occupational classification (SOC) and salary; employment and study undertaken after graduate and prior to survey activity. Details of these derivations will be published within the survey results coding manual.