Female graduates made the largest contribution to the total number of graduates over the previous five years. In fact, females made up 57.6% of higher education students in 2021/22, totalling 530,170. The number of females studying in higher education has increased by 13.5% since 2019/20.
Male numbers have also steadily increased from 332,925 in 2019/20 to 387,690 in 2021/22, a 16.4% increase in male graduates. Graduates who identify as other genders have also increased since 2019/20 from 1,000 to 2,080 in total. A large increase of around 108%. [34]
In the UK, typically business studies is the course with the highest number of graduates at a total of 141,125 graduates achieving a degree in this selected field in 2020. In second place, science subjects allied to medicine and biological sciences awarded 159,520 students with a degree in that same year. [1]
Subject | 2022 Graduates |
---|---|
Business and management | 175,670 |
Subjects allied to medicine | 99,570 |
Social sciences | 92,560 |
Design, and creative and performing arts | 65,380 |
Education and teaching | 65,355 |
Engineering and technology | 60,345 |
Law | 48,790 |
Biological and sport sciences | 36,175 |
Computing | 50,465 |
Psychology | 40,810 |
Language and area studies | 28,405 |
Historical, philosophical and religious studies | 27,345 |
Architecture, building and planning | 22,525 |
Medicine and dentistry | 21,535 |
Media, journalism and communications | 19,250 |
Physical sciences | 19,000 |
Mathematical sciences | 15,900 |
Geography, earth and environmental studies (natural sciences) | 10,675 |
Agriculture, food and related studies | 6,265 |
Combined and general studies | 6,800 |
Geography, earth and environmental studies (social sciences) | 4,655 |
Veterinary sciences | 2,480 |
At the other end of the scale, veterinary science courses typically have the fewest number of graduates in the UK with just 2,480 graduates entering the market in 2022.
The vast majority (71.5%) of undergraduates are white, while Asian students account for the second-largest demographic at 12.9%. The third-largest ethnicity demographic in 2022 was Black students (8.5%), followed by just under 1 in 20 (4.8%) who were from mixed ethnic backgrounds, as well as 2.3% who were from other minority ethnic groups.
Over a six-year period prior to 2022, the percentage of Asian, Black, Mixed and other ethnic groups beginning an undergraduate course has increased from 24% to 28.5%. [3]
Black students have seen the biggest increase in postgraduate study as figures rose from 5.8% in 2010/11 to 8.3% in 2019/20. Postgraduate entrants who were Asian rose by 2.4% from 9.4% over the same period.[4]
A higher proportion of white graduates (36.1%) complete their degree with a first-class qualification than any other ethnicity, closely followed by those with mixed ethnicity (31.3%).
The data below breaks down degree achievement by ethnicity:
Asian | 27.5% | 47.3% | 25.2% |
Black | 17.3% | 45.4% | 37.3% |
Mixed | 31.3% | 48.7% | 20% |
White | 36.1% | 46.9% | 17% |
Other | 25.1% | 47.6% | 27.3% |
According to the UK government, the percentage gap decreased from 26.3% to 20.0% between white and Black graduates getting a first-class or upper second degree from 2015 to 2022. The data suggests the trend is shifting but could take many more years to get to similar levels. [5]
On average, 6 in 100 (6.3%) students in the UK drop out of university. Since 2007/08, dropout rates have decreased by 1.1%, from 7.3% in 2008, to 6.2% a decade later. [6] This would mean an average of 48,548 students each year drop out.
The graph below shows the universities with the highest dropout rates:
Rank | University | Percentage No Longer in Higher Education |
---|---|---|
1 | Arden University | 32.30% |
2 | Point Blank Music School | 19.20% |
3 | SAE Education Limited | 18.50% |
4 | London Metropolitan University | 16.00% |
5 | Richmond, The American International University in London | 15.80% |
6 | University of Bedfordshire | 15.70% |
7 | The London Institute of Banking & Finance | 15.70% |
8 | University Centre Peterborough | 14.60% |
9 | University of the Highlands and Islands | 14.00% |
10 | Regent's University London Limited | 13.70% |
The university with the highest dropout rate as of the 2019/20 year is Arden University with a dropout rate of 32.3%. This is followed by Point Blank Music School at 19.2% [7]
The universities with the lowest dropout rates in the country as of 2019/20 are the Royal Veterinary College (0.4%), the University of Cambridge (0.6%), and the University of St Andrews (0.6%). [7]
Rank | University | Percentage of Students No Longer in Higher Education |
---|---|---|
1 | The Royal Veterinary College | 0.40% |
2 | The University of Cambridge | 0.60% |
3 | The University of St Andrews | 0.60% |
4 | The University of Oxford | 0.90% |
5 | The University of Durham | 1.30% |
6 | The University of Bristol | 1.50% |
7 | Imperial College of Science, Technology and Medicine | 1.50% |
8 | The University of Birmingham | 1.60% |
9 | London School of Economics and Political Science | 1.60% |
10 | Glasgow School of Art | 1.70% |
Studies show that the majority of universities with the lowest dropout rates can be found in the top 20 on the Complete University Guide’s league table. St George’s and the University of London are just two universities that have one of the lowest dropout scores and don’t sit in the top 20 league table.
IT and computing degrees, such as computer science, have the highest dropout rates with 9.2% not making it.
Undergraduate courses with the highest dropout rates were as follows: [7]
Rank | Subject | Dropout Rate |
---|---|---|
1 | Computer sciences | 9.20% |
2 | Business and administrative studies | 8.60% |
3 | Mass communications and documentation | 7.60% |
4 | Creative arts and design | 7.60% |
5 | Subjects allied to medicine | 7.50% |
6 | Combined subjects | 7.2 |
7 | Agriculture and related subjects | 7% |
8 | Engineering and technology | 7% |
9 | Architecture, building and planning | 6.90% |
10 | Biological sciences | 6.80% |
In 2020, 29,360 students in the UK deferred their studies for a gap year due to the pandemic, a 9.4% increase compared to 2019. [8]
Overall, universities in the UK awarded 133,995 first-class honours qualifications in 2022, a 14.1% decrease from the previous year where 155,955 were awarded.
We ranked data based on which universities awarded the most first class honours awards in 2022 below, with the University of Manchester coming out on top with 2,855 given. [9]
Rank | University | First-class degrees (2022) |
---|---|---|
1 | The University of Manchester | 2,855 |
2 | The University of Leeds | 2,750 |
3 | The Manchester Metropolitan University | 2,730 |
4 | University of Nottingham | 2,575 |
5 | University College London | 2,550 |
6 | The Open University | 2,505 |
7 | Coventry University | 2,485 |
8 | King's College London | 2,100 |
9 | The University of Birmingham | 2,050 |
10 | The University of Exeter | 2,050 |
The University of Leeds ranked second awarding 2,750 first-class honours classifications.
Overall, 197,345 upper second class honours qualifications were awarded in 2020/2021, a 4.1% increase compared to the year before.
The University of Cambridge has the highest employability ranking for the 2022 academic year, with the University of Oxford coming in second.
The table below shows the UK universities with the highest employability rank in the 2022 academic year. [32]
University | UK Employability Rank (2022) |
---|---|
University of Cambridge | 1 |
University of Oxford | 2 |
Imperial College London | 3 |
London School of Economics and Political Science | 4 |
London Business School | 5 |
King's College London | 6 |
University of Manchester | 7 |
UCL | 8 |
University of Edinburgh | 9 |
University of St Andrews | 10 |
University of Birmingham | 11 |
Durham University | 12 |
Cardiff University | 13 |
Brunel University London | 14 |
Five institutions based in London ranked in the top ten for the best career prospects in the UK. Imperial College London scored the highest (95%) based on the success of graduates after leaving university with the London School of Economics and Political Science (LSE) scoring slightly lower at 91%. [10]
The graph below shows the top ten universities in the UK ranked by their career prospects:
Whilst many graduates full of ambition go on to secure a full-time role, many also opt for a different path and become business founders. The best UK university for the proportion of business founders was found to be the University of the Arts London (15.83%), followed by LSE (14.03%).[11]
The table below shows the top ten universities for producing business founders ranked by the percentage of founders that came from there:
University of the Arts London | 15.83% |
London School of Economics and Political Science (LSE) | 14.03% |
Goldsmiths, University of London | 12.25% |
Falmouth University | 12.09% |
University of Oxford | 12.06% |
University of Cambridge | 12% |
Soas University of London | 11.47% |
University College London (UCL) | 10.41% |
University for the creative arts | 10.13% |
Middlesex University [E] | 10.11% |
The percentage of business founders is made from the number of graduates from that institution who go on to become business founders.
Graduate schemes are designed to help someone gain industry experience whilst earning a competitive salary. The aim of the scheme is to allow new candidates to explore various areas of a business in order to broaden their knowledge whilst following a structured program, leading to a successful career for many.
Companies such as Google, KMPG, NHS, and the UK Civil Service are just a few of many large corporations offering graduate schemes on a yearly basis in Britain.
Graduate schemes require a minimum grade in order to be considered for the role. In most cases, a 2.1 or higher is the grade most companies will look for when recruiting new candidates .
Recent reports show that students on average were applying up to 29 graduates schemes with different employers.
“The NHS receives an average of 85 applications for every graduate position”
In terms of NHS graduate schemes, reports show the public health service receives around 85 applications for each graduate position; they have 17,000 applications for just 200 spaces. [12]
On average, reports show that overall graduate job applications have increased by 41% from 2020 to 2021. [13]
The average graduate scheme salary is £30,362 and ranges between £25,000 and £32,000 according to Glassdoor [14] .
If you require more information on this topic, our research team has also compiled a complete analysis of graduate salaries in the UK including a comparison to non-graduates and a variety of job-specific data.
As you would expect, London pays the highest starting salary for graduates at £31,423, with the East of England following at £26,216. In the UK, Northern Ireland pays the least at an average of £22,143 to start. [15]
The table below shows the average starting salary for graduates around the UK:
London | £31,423 |
East of England | £26,216 |
South East | £26,180 |
South West | £25,722 |
Scotland | £25,476 |
North East | £25,034 |
West Midlands | £24,877 |
North West | £24,762 |
Wales | £24,758 |
East Midlands | £24,617 |
Yorkshire and The Humber | £24,448 |
Graduate starting salaries can clearly vary with a clear financial divide between the North and South of the UK. The total average salary in the UK is £29,669 as of 2023, therefore, only London graduates meet and exceed this.
The highest-paid graduate scheme in the UK is a private finance role for Rothschilds, with a salary of £65,000 followed by investment banking positions with J.P. Morgan, who will pay graduates £58,000 a year. [16]
Rothschilds | Private Finance | £65,000 |
J. P. Morgan | Investment Banking | £58,000 |
Goldman Sachs | Investment Banking | £52,000 |
The Royal Bank of Scotland (RBS) | Investment Banking | £54,000 |
Most commonly, the highest-paid graduate schemes sit within finance and banking and often exceed the average amount by tens of thousands of pounds.
Studies show that the majority (64%) of undergraduates undertake some period of unpaid work experience, with 41% of this group doing so for at least one month. [17]
In 2020, that would mean around 512,227 (64%) students did some period of work experience before entering the job market as graduates, and 210,014 did so unpaid.
“Over 210,000 students work unpaid during their degrees each year”
A separate study from 1995 showed that 82% of undergraduates had some work experience during their degree, comparatively this could indicate modern students are less likely to do work experience. [18]
In terms of employability, most (94%) interns are offered job roles once they have completed work experience, therefore, it is a valuable decision for students to add to their CV. [17]
Securing a job role is a process many students start prior to graduating with many beginning applications in an attempt to begin full-time work as soon as possible once qualified.
Studies show the time it takes for graduates to secure a job varies anywhere between three to 18 months.
In 2023, 82% of UK graduates from 2021 were in full-time or part-time employment. That’s around 314,450 graduates employed from that year. [33]
“A typical graduate cohort will see around 82% enter into employment or further studies within a year”
Below is an example of typical graduate outcomes showing the many different paths students choose once they have completed their degrees. The data refers to the graduates who graduating in 2020 and what they were doing when data was analysed in 2022.
Graduate Outcome | Number of Graduates |
---|---|
Full-time employment | 230,595 |
Part-time employment | 43,785 |
Unknown pattern of employment | 2,610 |
Voluntary or unpaid work | 4,825 |
Employment and further study | 42,330 |
Full-time further study | 33,395 |
Part-time further study | 2,260 |
Unknown pattern of further study | 290 |
Other including travel, caring for someone or retired | 19,530 |
Unemployed and due to start work | 4,275 |
Unemployed and due to start further study | 1,340 |
Unemployed | 18,605 |
Using 2021 graduates the data shows a typical outcome for a year’s cohort of students: the majority 89% are in some form of education or employment, a further 5% go into caring for loved ones and/or travel, while the remaining 5% are unemployed, or unknown.
Degrees related to professional services, tech , and science, had the highest percentage of high-skilled graduates employed in the UK at 93%, closely followed by information and communication professionals (90%).[19] Therefore, the degrees with the highest employment in the UK, are likely those in I.T., tech, and science related fields.
Below is a table which displays the percentage of graduates and/or postgraduates working in various professional industries by the level of skill, giving some indication of employment by degree subject:
Professional, scientific and technical activities | 93% | 6% | 1% |
Information and communication | 90% | 5% | 4% |
Education | 88% | 12% | 1% |
Mining and quarrying | 87% | 8% | 4% |
Human health and social work activities | 83% | 16% | 1% |
Public administration and defence; compulsory social security | 82% | 15% | 2% |
Activities of extraterritorial organisations and bodies | 82% | 16% | 2% |
Manufacturing | 80% | 13% | 8% |
Construction | 77% | 17% | 6% |
Real estate activities | 77% | 19% | 5% |
Water supply; sewerage, waste management and remediation activities | 75% | 15% | 10% |
Financial and insurance activities | 75% | 20% | 5% |
Arts, entertainment and recreation | 69% | 21% | 10% |
Other service activities | 66% | 29% | 5% |
Electricity, gas, steam and air conditioning supply | 64% | 14% | 21% |
Administrative and support service activities | 56% | 24% | 20% |
Transportation and storage | 41% | 21% | 38% |
Wholesale and retail trade; repair of motor vehicles and motorcycles | 34% | 8% | 58% |
Agriculture, forestry and fishing | 33% | 29% | 38% |
Accommodation and food service activities | 17% | 16% | 67% |
Activities of households as employers; undifferentiated goods-and services-producing activities of households for own use | 7% | 91% | 2% |
9 in 10 (90%) postgraduates who completed their course in 2018/2019 were in high-skilled jobs in 2021. In comparison to this, undergraduates equated to 69% suggesting that postgraduate study makes a prospective employee more likely to be hired.
In 2021, 13,275 graduates (from 2018 onwards) were self-employed , using graduate figures, that would mean only 0.42% of graduates from four academic years became self-employed after graduating.[19]
The table below demonstrates graduate numbers (from two academic years) for self-employment, and those running their own business, or working on creating projects.
Region | Self-employed/ freelance | Running own business | Developing a creative portfolio | Multiple |
---|---|---|---|---|
England | 11,400 | 4,360 | 16,760 | 11,735 |
Wales | 550 | 200 | 940 | 615 |
Scotland | 880 | 335 | 1,270 | 950 |
Northern Ireland | 420 | 130 | 730 | 445 |
Other UK | 25 | 10 | 35 | 30 |
Total UK | 13,275 | 5,035 | 19,740 | 13,770 |
The average graduate salary in the UK is £30,000 and has been this way since 2015. As we’ve discussed, this varies per region, and graduate schemes often inflate the average when compared to an entry-level role outside of such schemes.
Data from the UK government states that as of 2022, the median nominal graduate salary in the UK is £38,500, however, when adjusted for inflation, the median real-terms salary for graduates is £26,500. [30]
Science-based subjects such as medicine, dentistry, subjects allied to medicine, biological science, and veterinary sciences pay the highest salary to graduates who achieve a first-class degree and are highly skilled with starting salaries ranging between £31,000 to £35,000. [19]
A medium skilled dentistry role could be a dental assistant, while the high-skilled role in the field could be a dentist. This designation is not a choice we have made in the analysis and comes from the various data sources.
Medicine & dentistry | £35,000 | £18,500 | N/A |
Subjects allied to medicine | £25,000 | £19,000 | £18,000 |
Biological sciences | £23,000 | £18,500 | £18,000 |
Veterinary science | £31,000 | N/A | N/A |
Agriculture & related subjects | £23,500 | £18,500 | £19,000 |
Physical sciences | £25,000 | £19,500 | £18,000 |
Mathematical sciences | £28,000 | £20,500 | £17,000 |
Computer science | £27,000 | £20,000 | £18,000 |
Engineering & technology | £28,000 | £22,000 | £18,000 |
Architecture, building & planning | £24,000 | £20,000 | £18,000 |
Social studies | £26,000 | £20,000 | £18,000 |
Law | £22,000 | £19,500 | £18,000 |
Business & administrative studies | £25,000 | £20,500 | £18,000 |
Mass communications & documentation | £21,000 | £19,500 | £17,000 |
Languages | £24,000 | £19,500 | £18,000 |
Historical & philosophical studies | £24,500 | £19,000 | £18,000 |
Creative arts & design | £21,000 | £18,000 | £16,000 |
Education | £24,500 | £18,000 | £17,000 |
Combined | £26,000 | £19,000 | £18,000 |
Total average | £25,000 | £19,500 | £18,000 |
For most roles, if graduates possess a lower range of skills when starting out, the average salary earned is £18,000 with the potential to progress as skillsets are improved.
The best university for high salaries is the University College of Estate Management (UCEM), as 29% of its graduates receive a salary of £51,000 or more. This is a remote learning institution specialising in construction and real estate graduates, leading to roles that have higher salaries.
London School of Economics (LSE) is the next highest university with 12% of its graduates hitting the £51,000+ salary mark. [13] In 2021, LSE was offering 41 undergraduate courses therefore not having a specialised pool of undergraduates like UCEM.
The following is a list of the top ten universities ranked by the percentage of graduates earning above £51,000:
University College of Estate Management | 29% |
London School of Economics and Political Science | 12% |
BPP University | 11% |
Birkbeck College | 9% |
Imperial College of Science, Technology, and Medicine | 8% |
The Open University | 8% |
The University of Cambridge | 7% |
Arden University | 6% |
The University of Oxford | 6% |
The University of Warwick | 5% |
The highest published graduate starting salaries for 2021 include law firms White & Case (£50,000), Clifford Chance (£48,000), Baker McKenzie (£48,000), Linklaters (£47,000), technology company TPP (£45,000) and retailer Aldi (£44,000.) [13]
Other studies [31] on universities with the highest graduate salaries have shown:
Male full-time high-skilled graduates who obtained first-class degree qualifications and entered full-time paid employment in the UK in 2019 were paid on average £26,000 compared to high skilled females who were paid £24,500. [19]
On average, male graduates at the age of 25 earn 5% more than the average female graduate. By age 30, the gender pay gap in annual earnings stands at 25%. [20]
The graph below demonstrates the portion of graduates and their earnings for men and women. [19] Studies do not offer comparative information for anyone identifying as other than male or female.
Lower salary bands on average have a higher percentage of women earning more than men. Whilst an average of 6% more women than men earned more in the salary bracket £24,000 – £26,999, it seems that the gender pay gap widens as the salary band increases. In particular, 7% of males earn £51,000+ compared to just 3% of females.
The graduate unemployment rate is 12.7% for graduates who obtained a degree in recent years (2020 onwards). In 2021, the graduate unemployment rate was 12%, meaning the rate has decreased by 0.7 percentage points.[30]
With the rate of 12%, this would mean, in recent years, there are approximately 96,041 unemployed graduates each year in the UK, based off 2021’s academic year.
When looking at the data regionally, we can see that England, Wales, and Scotland have an average graduate unemployment rate of 5%, while Northern Ireland has just 3%. [2]
Most graduates who apply for graduate schemes and job roles will never receive a response from the majority of their applications. With most graduates applying on average to around 25 job roles, they find that the ratio of receiving an invitation to an interview to job applications is just over twenty to one .
There are some degree subjects that are more likely to land you a job when you graduate than others. One study analysed the employability of different degrees by asking students to give a rating on the job prospects available to them after graduating.
Media and cultural studies came out as the worst degree subject for employment with students rating it a 2.5/5 on average. As a broad degree, students found that jobs in media were highly competitive and applicants with more specific skills generally fared better.
The degree subject with the second lowest employability rating was European studies (2.8/5). Similar to international relations (3/5), graduates in this sector are often competing for a small number of roles in global organisations, making it a tough job market.
Other subjects that students rated poorly for job prospects were Psychology (2.9/5), which often requires further study to obtain a job, and civil engineering (3.1/5) which saw a 50% decrease in new apprentices in 50% as the sector has struggled to offer new jobs.[28]
Rank | Subject | Rating for job prospects |
---|---|---|
1 | Media and cultural studies | 2.5/5 |
2 | European studies | 2.8/5 |
3 | Psychology | 2.9/5 |
4 | International Relations and European Studies | 3/5 |
5 | Civil Engineering | 3.1/5 |
The pandemic had a detrimental impact on the opportunities available and career prospects for everyone, but especially graduates who entered the market at the time.
As many were ordered to stay indoors, 75% of graduates noticed a fall in the number of available opportunities in 2020 and 2021, with many (72.6%) graduates feeling less confident about their future. [38] According to the University of Southampton, 83% said that the pandemic had a detrimental impact on graduates’ employment prospects with most graduates saying they had to rethink their future (79.4%). [21]
Furthermore, the ONS found that UK graduates reported a lower life satisfaction score (6.7) than people of their typical age (6.9) and average adults (7.0) during that time. [22]
The pandemic also saw many more graduates in roles that aren’t typically filled by people with degrees. In the UK in 2020, 25.5% of graduates were in a role that was requiring skills less than they had gained in their degree, referred to as a ‘skills mismatch’, this was 5% more in 2019 during the pandemic. [2]
Fees to study at university were first introduced in 1998. In 2006, a new system was introduced which would make studying for a degree more accessible. The cost to study was raised to £3,000 in England which would be classed as a tuition loan. As fees gradually increased, the Government raised fees further to £9,000 per year in 2012.
Whilst the pandemic caused record unemployment, graduates were also unable to make repayments. [40] In fact, 201,900 graduates (From various academic years) failed to make repayments through their salary in 2020-21.
As students in England pay £9,000 a year to study for their chosen degree, they will incur an average of over £46,150 of student loan debt in the 2021/22 year.
In comparison, the average student loan debt varies in other regions, for example, Wales has an average of £33,830, and Northern Ireland has £24,360. Scotland has the lowest average student loan debt at £14,840, this is due to Scottish students not having to pay tuition fees. [23]
Whilst the new system implemented in 2012 allows students to defer their student loans during their study, these debts still have to be repaid within 30 years.
The table below shows a graduates minimum earnings (before tax) to have to pay back their student debts:
England | £27,295 | £2,274 |
Scotland | £25,000 | £2,083 |
Wales | £27,295 | £2,274 |
Northern Ireland | £19,895 | £1,657 |
As you can see, England has the highest repayment threshold allowing graduates to earn up to £27,295 a year before having to start making repayments. In comparison, Northern Ireland has the lowest threshold with graduates beginning repayments once earning £19,895.
The average annual repayment via HMRC per region in order was England (£930), Northern Ireland (£840), Wales (£800), and finally, Scotland (£660.)
As students start making repayment at different thresholds, the average time to pay off a student loan may vary per person. A study revealed that a student loan takes an average of 29 years and 4 months to pay off with the average debt being just under £48k in England. [25]
According to the UK Government, it is expected that just 25% of current full-time undergraduates will repay their full student loan. [26]
The Students Loan Company states that as of 2021, outstanding student loan debt in the United Kingdom reached over 177 billion British pounds starting from 2013/14 when student debts were increased to £9,000 a year.
“Estimates say by 2050, there will be £560 billion of student loan debt in the UK”
England owes the majority of this debt standing at £160 billion with Scotland totalling £6.5 billion, Wales £6.2 billion, and Northern Ireland £4.1 billion. By the middle of the century, the Government predicts that the value of outstanding loans will be around £560 billion. [27]
In 2021, 1,117,000 students took a student loan. The value of student loans equalled £15,908 million.
[1] HESA: Higher Education Student Statistics: UK https://www.hesa.ac.uk/data-and-analysis/students
[2] ONS: Graduates’ labour market outcomes during the coronavirus (COVID-19) pandemic https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/graduateslabourmarketoutcomesduringthecoronaviruscovid19pandemicoccupationalswitchesandskillmismatch/2021-03-08
[3] Gov: First year entrants onto undergraduate study
https://www.ethnicity-facts-figures.service.gov.uk/education-skills-and-training/higher-education/first-year-entrants-onto-undergraduate-and-postgraduate-degrees/latest/
Accessed February 2024
[4] Office for Students: Equality, diversity, and student characteristics data.
https://www.officeforstudents.org.uk/data-and-analysis/equality-diversity-and-student-characteristics-data/
[5] UK Gov: Undergraduate degree results
https://www.ethnicity-facts-figures.service.gov.uk/education-skills-and-training/higher-education/undergraduate-degree-results/latest
[6] Debut: Degree Dropouts
https://debut.careers/degree-dropouts/
[7] HESA: UK Higher Education Performance Indicators –
https://www.hesa.ac.uk/data-and-analysis/performance-indicators/non-continuation
[8] Teaching Abroad Direct: Gap Year Statistics UK
https://www.teachingabroaddirect.co.uk/blog/gap-year-statistics-uk
[9] HESA: HE qualifiers by HE provider and level of qualification obtained 2014/15 to 2021/22
https://www.hesa.ac.uk/data-and-analysis/students/table-16
[10] Complete University Guide: University League Tables 2022
https://www.thecompleteuniversityguide.co.uk/league-tables/rankings?sortby=graduate-prospects
[11] Hitachi Capital Invoice Finance: Which university produces the most CEOs? Via
https://www.businessleader.co.uk/which-university-produces-the-most-ceos/#
[12] NHS: Multi-award winning Graduate Management Training Scheme doubles its intake
https://www.hee.nhs.uk/news-blogs-events/news/multi-award-winning-graduate-management-training-scheme-doubles-its-intake
[13] Highfliers: The Graduate Market in 2021
https://www.highfliers.co.uk/download/2021/graduate_market/GM21-Report.pdf
[14] Glassdoor: Graduate Scheme Salaries in the UK
https://www.glassdoor.co.uk/Salaries/graduate-scheme-salary-SRCH_KO0,15.htm
[15] Prospects Luminate: Graduate Salaries in the UK
https://luminate.prospects.ac.uk/graduate-salaries-in-the-uk
[16] Glide: Highest Paid Graduate Schemes
https://glide.co.uk/guides/highest-paid-graduate-schemes/
[17] Prospects: Students urged to focus on longer work experience for employability boost
https://www.prospects.ac.uk/prospects-press-office/students-urged-to-focus-on-longer-work-experience-for-employability-boost
[18] HEFCE: Nature and extent of undergraduates’ work experience
https://dera.ioe.ac.uk/5159/1/rd19_02.pdf
[19] HESA: Graduates’ salaries
https://www.hesa.ac.uk/data-and-analysis/graduates/salaries
[20] IFS: Gender differences in subject choice lead to gender pay gap immediately after graduation
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High rates of postgraduate researchers (PGRs) terminate their studies early. This attrition can have detrimental personal consequences, and results in a loss of productivity, and research and innovation for the higher education sector and society as a whole. PGRs are vulnerable to the experience of mental health problems; a factor that appears to be increasing attrition amongst students in the UK. However, investigation of the determinants of problems with PGRs’ attendance and influencing intention to discontinue their studies is rare. Here, we consider the relative predictive validity of a set of putative predictors (mental health symptoms, demographic, occupational, psychological, social, and relational) of attendance behaviours (absenteeism, presenteeism, mental health-related intermission) and early attrition intention amongst UK PGRs. Depression, anxiety, and suicidality predicted attendance behaviours and greater attrition intention. Individual demographic and occupational factors predicted all outcomes. Psychological, social and relational factors had less predictive validity, although individual variables in these conceptual clusters did significantly predict some outcomes. Our results suggest that interventions to reduce high rates of mental health problems are likely to improve attendance behaviours, and reduce the extent to which PGRs intermit or consider ending their PhD studies for mental health-related reasons. Initiatives designed to improve supervisory relationships and reduce loneliness may also reduce absenteeism, intermission and attrition intention.
Evaluating mental health and wellbeing of postgraduate researchers: prevalence and contributing factors.
Avoid common mistakes on your manuscript.
Doctoral attrition is high in many countries, with reported rates of up to 40 to 50% of postgraduate researchers (PGRs) terminating their PhD studies before completion (Geven et al., 2017 ; Litalien & Guay, 2015 ). Attrition can be considered a process, in which PGRs weigh the costs and benefits of persisting or discontinuing, and then do or do not actually end their studies accordingly (Jaksztat et al., 2021 ). Attrition may be provoked by reasons outside of the PhD, such as other opportunities, changing goals, or family obligations (Maher et al., 2017 ), and is not in itself ‘bad’. However, for many PGRs, attrition results in adverse psychological, financial and employment outcomes, as well as there being problematic consequences of non-completed PhD research for supervisors, institutions, and society (Litalien & Guay, 2015 ).
There has been limited research attention to PGR attrition (Jaksztat et al., 2021 ; Litalien & Guay, 2015 ), especially as it relates to mental health problems and other psychological factors (Jaksztat et al., 2021 ). The few known studies that have been conducted in this area originate from the US, where the PhD experience is unique; for example, typically being more structured and longer in duration (Jaksztat et al., 2021 ). This lack of research is especially concerning as increasing numbers of university students generally in the UK appear to be discontinuing their studies due to poor mental health (The Guardian, 2017 ).
Much evidence has emerged in the past few years in particular to suggest that PGRs experience high rates of stress (Hazell et al., 2020 ), depression, anxiety and suicidality; seemingly at rates that exceed those seen in other student and working populations (Hazell et al., 2021 ; Levecque et al., 2017 ). The PhD itself and its surrounding environment have been implicated in the onset and exacerbation of PGR mental health problems (Berry et al., 2020 ; Levecque et al., 2017 ), with the recent COVID-19 pandemic seemingly exacerbating the poor mental health and wellbeing of the adult population generally (Amerio et al., 2021 ; Odone et al., 2020 ), and PGRs specifically (Byrom, 2020 ) even further.
Poor mental health and suicidality are inherently distressing and associated with negative health and functional outcomes (DeRoma et al., 2009 ; Okajima et al., 2015 ), including death by suicide; a frequently under-reported phenomenon (Visentin et al., 2019 ). A functional outcome for PGRs that is important, yet under-investigated, is mental health-related attrition. However, attrition is not the only index of disengagement from academic study. Disengagement can be conceptualised multi-dimensionally, with a focus on problematic attendance behaviours as well as attrition-related cognitions. Attendance behaviours themselves can be conceptualised as using multiple indices: absenteeism (non-planned/non-holiday absences); presenteeism (working or studying when unwell enough to take absence); and intermission (interruption or prolonged break from doctoral studies). Evidence suggests that PGRs with experience of mental health problems report greater absenteeism and presenteeism (Berry et al., 2021a ), intermission, and intention to discontinue doctoral study (Castelló et al., 2017 ; González-Betancor & Dorta-González, 2020 ; Hunter & Devine, 2016 ).
The present study tests a comprehensive range of potential determinants of attendance behaviours and attrition intention amongst a large sample of UK PGRs. We were informed by our previous work which identified putative determinants of mental health symptoms as demographic, occupational, psychological, social and relational in nature (Berry et al., 2021b ). Taking this same approach here aligns with studies that suggest influences on doctoral attrition are complex and multifactorial (Castelló et al., 2017 ; Gardner, 2010 ), and with theoretical models of student retention as being a product of both academic and social integration (Tinto, 2016 ). We first predicted that mental health symptoms would predict poorer attendance and attrition intention.
We next predicted that demographic and occupational factors would predict attendance and attrition intention. White PGRs report fewer days absent but more severe presenteeism (Berry et al., 2021a ). Female PGRs appear to spend more time in presenteeism and absenteeism (Berry et al., 2021a ), report greater number of intermissions (Moore & Keith, 1992 ), greater attrition intention (Castelló et al., 2017 ), and are more likely to actually discontinue their PhD studies (Jaksztat et al., 2021 ). A lack of funding is associated with greater attrition intention (Castelló et al., 2017 ) and attrition (Litalien & Guay, 2015 ). PGRs who spend less time per week in PhD study report greater attrition intention (Castelló et al., 2017 ). However, it is unclear to what extent demographic and PhD-study related characteristics independently and uniquely influence attendance behaviours and attrition intention, i.e. when modelled simultaneously and when considering psychological and social factors.
Finally, we predicted that psychological, social and relationship factors would predict PGR attendance behaviours and attrition intention. With respect to psychological factors, PGRs who perceive themselves to lack competence report greater attrition intention (Castelló et al., 2017 ) and attrition (Litalien & Guay, 2015 ). Moreover, lower academic aspirations are associated with a greater number of intermissions (Moore & Keith, 1992 ). This suggests a potential role in doctoral attrition for the psychological traits of perfectionism ( i.e. having high standards and/or believing one is not meeting their standards) and impostor thoughts ( i.e. believing that one is not as competent as others perceive one to be). Moreover, the nature of interpersonal relationships in general, and specifically with the supervisor, are likely important. Social disconnectedness is associated with PGR attrition intention (Castelló et al., 2017 ; Volkert et al., 2018 ). Supervisory relationship stressors and lack of psychological support are associated with attrition intention (Litalien & Guay, 2015 ; Volkert et al., 2018 ), and clear authoritative direction, i.e . supervisor agency, seems important in doctoral completion (McCray & Joseph-Richard, 2020 ). Mental health problems may be a confounding factor here, however, as previous research has found supervisory relationship qualities to predict mental health symptoms (Berry et al., 2021b ) and thus, this should be accounted for in modelling associations with attendance and attrition-related outcomes.
Research considering the relative contribution of influences across multiple domains on PGR disengagement is rare, especially considering indices spanning multiple proxies of attendance and attrition intention. Furthermore, although research has considered whether dissatisfaction is a precursor to attrition (González-Betancor & Dorta-González, 2020 ), no known study has tested whether problematic attendance behaviours themselves may function as precursors to attrition intention. We predicted that this would be the case because academic disengagement leads to intention to leave academia (Lesko & Corpus, 2006 ). Furthermore, absence and presenteeism reduce productivity (Johns, 2010 ), organisational commitment and embeddedness (Boswell et al., 2008 ), and additionally likely increase time-to-completion, which in turn predicts doctoral attrition (de Valero, 2001 ).
Based upon our predictions, we tested the specific hypotheses that attendance behaviours and attrition intention would be predicted by mental health symptoms (depression, anxiety, suicidality), and then by the following factors:
demographic; age, gender, ethnicity, UK residency, disability and lifetime mental health problem prevalence,
occupational; fulltime status, funding, year of study, fieldwork, time spent in occupational activity,
psychological; impostor thoughts, perfectionistic standards and discrepancy,
social; loneliness and multiple group memberships, and
relational; supervisory relationship communion and agency.
Participants and procedure.
Data were obtained from a national online self-report survey (U-DOC) conducted in the UK between 2018 and 2019. The survey was designed to contain a battery of self-report survey assessments and qualitative data pertaining to PGR mental health symptoms, and multiple factors considered to be potential correlates of these symptoms and associated behaviours. Participants were a convenience sample of 3352 current PGRs who provided informed consent and then completed questionnaire measures and qualitative free-text questions. The participant inclusion criteria were that participants were aged 18 years or over and were currently studying for their PhD at a UK University. Participants were recruited by contacting all UK doctoral schools ( N = 162) and asking for them to promote the study, via email, institutional communications and through social media advertising (e.g. Twitter, Facebook). The research team additionally promoted the study via social media platforms. The study received research ethics approval from the University of Sussex Sciences and Technology Cross-Schools Research Ethics Committee (Reference: ER/CH283/9). Additional methodological and sample details are reported elsewhere (Authors, 2021).
Absenteeism and presenteeism data were collected using items from the Institute for Medical Technology Assessment Productivity Cost Questionnaire (iMTA PCQ) – Presenteeism Scale (Bouwmans et al., 2015 ). For the present study, binary variables were used to indicate absenteeism (no absenteeism 0, absenteeism 1) or presenteeism (no presenteeism 0, presenteeism 1) specifically regarding PhD study in the past month, excluding planned annual leave or holidays. Absenteeism referred to days absent and presenteeism referred to “days in which you worked but during this time were bothered by physical or psychological problems”. Additional information about the measure of absenteeism and presenteeism has been published previously (Berry et al., 2021a ).
Respondents indicated if they had had to take a break from their PhD studies for mental health-related reasons (mental health-related intermission), and if they had considered terminating their PhD studies for mental health-related reasons (mental health-related attrition intention). In both cases, respondents indicated whether statements were ‘true’ or ‘false’; coded as ‘1’ or ‘0’, respectively. A third option ‘not sure’ was also coded as ‘0’.
The 9-item Patient Health Questionnaire (PHQ-9 (Kroenke et al., 2001 )) was used to capture depression symptoms, the 7-item (GAD-7 (Spitzer et al., 2006 ) to capture anxiety symptoms, and the 4-item Suicide Behaviors Questionnaire – Revised (SBQ-R (Linehan & Nielsen, 1981 )) to capture suicidality.
Participants self-reported age in years, gender (coded for this study as female versus male/another identity), ethnicity (coded as White versus non-White), UK citizenship ( versus non-UK citizenship), disability status (not including mental health problems), and lifetime prevalence of mental health problems (coded as pre-existing mental health problems up to and including during undergraduate studies versus onset during postgraduate study).
Participants were asked to self-report their PhD study mode (fulltime versus part-time), funding (full, partial or self-funded), year of study, and past or planned fieldwork ( versus none). Participants estimated how many hours per week on average they spent engaged in PhD study, teaching activities, and any other employment. These were summed to create weekly average of hours spent in occupational activity.
Impostor thoughts were measured using the 20-item Clance Impostor Phenomenon Scale (CIPS (Clance, 1985 )), and perfectionistic standards ( i.e. high expectations for oneself) and discrepancy ( i.e. the degree to which one thinks they fail to meet these expectations) using the 8-item Short Almost Perfect Scale (SAPS (Rice et al., 2014 )).
The social variables captured were loneliness ( i.e. the subjective sense of deficiency in one’s social relationships), and multiple group memberships ( i.e. the degree to which one perceives they have ties and relationships with multiple social groups). Loneliness was measured using the 20-item UCLA Loneliness Scale (Russell et al., 1978 ). Multiple group memberships was captured using a 4-item self-report scale derived from the Exeter Identity Transition Scale (Haslam et al., 2008 ).
The relational qualities of the supervisory relationship were measured using the 41-item Questionnaire on Supervisor–Doctoral student Interaction (QSDI (Mainhard et al., 2009 )). Two dimensional scores were used; agency (influence and leadership) and communion (proximity and cooperativeness).
All analysis was conducted in SPSS (version 26.0). Bivariate associations between putative predictor variables, attendance behaviours and attrition intention were examined using t-test and chi-square models. Hierarchical logistic regression was used to test predictors of attendance behaviours and attrition intention in four separate models. Attendance behaviours and attrition intention were specified as binary categorical dependent variables in these models. Mental health symptom scores were added as predictors first, before then entering the demographic, occupational, psychological, social and relational factors in turn as separate blocks. Variables not showing bivariate associations with attendance behaviours and attrition intention were not entered. Hochberg’s correction was applied for multiple testing in the final models (Menyhart et al., 2021 ). These models met the requisite assumptions of the independence of errors and absence of significant multicollinearity. Moreover, with the exception of two standardised residuals in the presenteeism model and one in the attrition intention model, all standardised residuals were under 2.5. Cook’s distances and DFbetas all under 1, suggesting, showed no significant impact of unusual cases on any model. The Box-Tidwell test was used to confirm that the relationship between the logit (log-odds) of the outcome and each continuous predictor was linear. All interactions between the predictors and their logits were non-significant, with the exception of depression (PHQ-9), anxiety (GAD-7), and supervisory agency and communion (QSDI) in one model respectively. However, these interactions were not highly significant (p ≥ 0.03) and the sample size is large, therefore, we considered the assumption of non-linearity to be satisfied (Wuensch, 2021 ).
Respondents were aged on average 30.74 (SD 8.82) years and 2205 (65.8%) were female. Overall, 1749 (52.2%) respondents identified as White British and 2114 (63.2%) as UK residents. In total, 1059 (31.8%) respondents reported having been given a diagnosis of a mental health disorder during their lifetime, and another 919 (27.6%) reported experiencing mental health problems with no formal diagnosis. The majority of students were fulltime ( n = 2536, 81.4%) and had full ( n = 2036, 65.4%) or partial funding ( n = 413, 13.3%). Eight-hundred and thirty-four PGRs were in their first year (26.9%), 846 (27.3%) in their second, 756 (24.4%) in their third, 422 (13.6%) fourth, and 144 (4.6%) in the fifth year of PhD studies. A significant minority of students reported past ( n = 767, 24.8%) or planned ( n = 303, 9.8%) fieldwork. In total, 1069 (31.9%) PGRs reported taking non-planned/holiday absence in the past month and 1697 (50.6%) taking no absences. Overall, 1694 (50.5%) PGRs reported presenteeism in the past month, and 1201 (35.8%) reported none. In addition, 455 (13.6%) of PGRs had taken mental health-related intermission and 2604 (77.7%) had not ( i.e . had rated this statement as false or not sure). Finally, 1097 (32.7%) of PGRs had considered ending their PhD studies for mental health-related reasons and 1963 (58.5%) had responded false or not sure. Ninety-seven PGRs (3.1%) in current continuation were removed from the dataset before analysis, for attendance behaviours and attrition intention were thought not to be equivalent in this context.
Bivariate associations (Tables 1 and 2 ) demonstrated that past month absenteeism and presenteeism, having taken mental health-related intermission, and reporting mental health-related attrition intention, were all significantly associated with having a disability (Table 1 ), and with significantly greater depression, anxiety, suicidality, impostor thoughts, perfectionistic discrepancy, loneliness, and reduced supervisory communion (Table 2 ). Past month absence and intermission were associated with significantly reduced weekly occupational activity hours, whereas past month presenteeism and attrition intention were associated with more hours (Table 2 ). Absenteeism and presenteeism, and attrition intention, but not intermission, were significantly associated with younger age (Table 2 ) and being female (Table 1 ). Only absenteeism was associated with reduced perfectionistic standards, and only presenteeism and attrition intention were associated with higher standards (Table 2 ). Presenteeism, intermission and attrition intention were associated with being White (Table 1 ) and reduced perception of multiple group memberships (Table 2 ). Absenteeism was more likely for non-White and non-UK citizens (Table 1 ). Taking intermission and reporting attrition intention were more likely for UK citizens and people with more recent-onset mental health problems (Table 1 ). Presenteeism did not differ according to UK citizenship or pre-existing mental health problems (Table 1 ). Absenteeism, intermission and attrition intention, but not presenteeism, were associated with past or planned fieldwork (Table 1 ), and with reduced supervisory agency (Table 2 ). Full-time PGRs were more likely to report absenteeism and presenteeism, whereas a part-time mode was associated with taking intermission, with no association between study mode and attrition intention (Table 1 ). Absenteeism and presenteeism, and attrition intention, were more likely for fully funded PGRs and less likely for self-funded students (Table 1 ). Taking intermission was less likely for fully funded PGRs, and more likely in partial or self-funded modes (Table 1 ).
The hierarchical logistic regression models (Table 3 ) showed that the predictor blocks explained significant variance in attendance behaviours and attrition intention, with some exceptions. First, social factors did not explain significant variance in any model, although this was marginal for the intermission model, in which loneliness was a significant predictor of intermission likelihood. Secondly, psychological and relational predictors did not explain significant variance in presenteeism or intermission. With respect to significant individual predictors when all blocks had been entered, past month absence (Table 3 , model A) was significantly predicted by greater depression, younger age, non-female gender, White ethnicity, UK citizenship, not having a disability, not being self-funded, reduced weekly occupational hours, no fieldwork, and reduced supervisory agency. Past month presenteeism (Table 3 , model B) was significantly predicted by greater depression and anxiety, being non-female and non-disabled, not being fully-funded, greater occupational weekly hours, and greater perfectionist standards. Having taken mental health-related intermission (Table 3 , model C) was significantly predicted by greater anxiety, pre-existing mental health problems, more years of PhD study, reduced impostor thoughts, and greater perfectionistic discrepancy and loneliness. Mental health-related attrition intention (Table 3 , model D) was predicted by greater depression and suicidality, being non-White, pre-existing mental health problems, more years of PhD study, reduced communion in the supervisory relationship, and not taking mental health related-intermission. All odds ratios reflected small size effects. The majority of individual predictors were significant at the respective Hochberg corrected alpha level (Table 3 ).
This study aimed to test mental health symptoms, and demographic, occupational, psychological, social and relational factors as predictors of PGR attendance behaviours (absenteeism, presenteeism, mental health-related intermission) and attrition intention. Our study, using cross-sectional data, shows that demographic and occupational factors are significant predictors of PGR attendance behaviours (absenteeism, presenteeism, mental-heath-related intermission) and attrition intention. This was evident across all models (bivariate and multivariate), though specific demographic and occupational factors differed in their patterns of prediction. Psychological, social and relational factors had less predictive validity, but made significant contributions to some models. The largest effects according to individual odds ratios were for demographic and supervisory relationship characteristics, and additionally for mental health-related intermission as a predictor of attrition intention.
With respect to demographic and occupational characteristics, absenteeism was predicted in the multivariate model by being White and a UK citizen. One interpretation of this is that ethnically diverse and international students take fewer absences because they feel under greater pressure to be present and to succeed in their PhD research (Litalien & Guay, 2015 ); pressure that may be both socio-cultural and bureaucratic as related to visa status. Not taking absences may in turn contribute to distress, for non-White and non-UK citizens were found to be more likely to consider terminating their studies early for mental health reasons. More years of PhD study predicted greater likelihood of mental health-related intermission and attrition intention, even when controlling for current symptoms. Current findings suggest that demographic factors play a greater role in predicting attendance behaviours and attrition intention, compared to their seemingly smaller role in predicting PGR mental health symptoms versus psychological, social and relational factors (Berry et al., 2021b ). It may be that demographic vulnerabilities have especially profound influence on behavioural outcomes. For example, socio-demographic characteristics influence the extent to which people experience stigma and discriminatory behaviours within academic institutions (Berry et al., 2021a );, factors that in turn influence academic disengagement (Casad et al., 2019 ). The associations with having a non-mental health disability were surprising. In the multivariate models, being disabled predicted reduced absenteeism and presenteeism, whereas in the bivariate associations, people with a disability reported greater presenteeism. This may reflect that the degree of flexibility provided by doctoral study, for example in working hours and locations, allows people with disabilities to work when best suits them, which reduces their absenteeism. With respect to presenteeism, mental health symptoms may be an additional explanatory factor. It might be that people with a disability report greater presenteeism mainly due to elevated mental health symptoms, and once these symptoms are covaried, this association reverses because this group can flexibly arrange their PhD study time around other health issues. This fits with the finding that PGRs feel less able to take absences for mental health compared to physical health reasons (Berry et al., 2021a ).
Overall, mental health symptoms predicted greater absenteeism, presenteeism, intermission and attrition intention. Depression consistently predicted all outcomes except intermission, with anxiety predicting greater likelihood of presenteeism and intermission, and suicidality predicting greater likelihood of presenteeism and attrition intention. These findings support previous studies demonstrating that mental health problems result in greater absenteeism and presenteeism (Berry et al., 2021a ), intermission (González-Betancor & Dorta-González, 2020 ), and intention to discontinue doctoral study (Castelló et al., 2017 ; Hunter & Devine, 2016 ). The predictive validity of depression is consistent with evidence that it predicts poor attendance and educational engagement, more so than anxiety and especially when persistent (Abu Ruz et al., 2018 ). That all symptoms predict presenteeism is intuitive, because presenteeism is defined as working when bothered by physical or psychological problems (Bouwmans et al., 2015 ). Regarding attrition intention, it seems likely that the co-influence of depression and suicidality here is related to hopelessness being implicated in both these problems (Beck et al., 2006 ; Labelle et al., 2013 ), and presumably in considering discontinuing PhD studies, especially in the absence of anticipated success. This aligns with qualitative data from the present sample that suggests suicidal ideation can occur in the context of PhD failure concerns, with suicide considered by some PGRs as potentially a more favourable hypothesised outcome than not completing their PhD (Authors, 2021). A previous study found that only the unique symptoms of anxiety, excluding those shared with depression, predicted educational attrition (Gorman et al., 2020 ). In the present study, this relationship was not observable for attrition intention, but anxiety alone uniquely predicted taking mental health-related intermission. It could be that anxious avoidance is the best predictor of taking intermission, with little independent role for symptoms of depression or suicidality. Alternatively, as current data are cross-sectional, the directionality of associations is not clear and it is possible that PGRs who have previously taken mental health-related intermission are then more anxious.
Whilst social factors were bivariately associated with attendance behaviours and attrition intention, loneliness and multiple group memberships contributed little to the prediction of attendance behaviours, with the exception of loneliness predicting mental health-related intermission. This is difficult to reconcile with prior research that suggests important roles for social and relational factors, albeit non-synonymous yet overlapping with those measured here. For example, it has been suggested that doctoral persistence is largely shaped by social interactions with peers and supervisors (Bean & Tinto, 1988 ; Litalien & Guay, 2015 ). Research evidence has additionally found that sense of belonging reduces attrition intention (van Rooij et al., 2019 ), and that the perceived institutional climate predicts time spent in absenteeism and presenteeism, and presenteeism severity (Berry et al., 2021a ). It could be that social factors indirectly influence behavioural outcomes and attrition intention via symptomatology. If this is the case, social interventions should still reduce absenteeism, presenteeism, and mental health-related intermission and attrition intention, through improving symptoms. This is in keeping with the resilience protection model of doctoral completion (McCray & Joseph-Richard, 2020 ), which suggests that complex inter-relations between personal, environmental, professional and institutional factors influence successful completion.
Psychological factors showed little predictive validity for attendance behaviours and attrition intention, other than that PGRs with higher perfectionistic standards were more likely to engage in presenteeism. This contradicts a previous study which found perceived competence to be the central factor in explaining PhD attrition (Litalien & Guay, 2015 ). However, this previous study did not control for mental health symptoms. It could be that associations between psychological factors and attendance behaviours and attrition intention are again indirect via associations with mental health symptoms. Lower supervisory agency and communion respectively predicted greater absenteeism and mental health-related attrition intention, which is in keeping with evidence that supervision quality is associated with intent to discontinue PhD studies (van Rooij et al., 2019 ). Our findings therefore caution against the seemingly prevailing view that PGR wellbeing and success are determined by students’ individual competencies, with more limited roles for supervisory and institutional characteristics and actions (Sverdlik et al., 2018 ). Indeed, supervisors with PGRs who discontinue early due to mental health problems might benefit from specific attention as to the sense of communion characterising their supervisory relationships, and training and initiatives to help enhance this where necessary.
Finally, absenteeism and presenteeism were not predictive of attrition intention, yet mental health-related intermission did appear to be significantly protective. It could be that an intermission period enables PGRs to put into place supports to help scaffold their mental wellbeing, which helps them to feel able to continue their doctorate to completion. It may also be that PGRs who have not taken intermission reflect those who feel unable or unwilling to take a period of intermission, and are perhaps then more likely to consider discontinuing their studies completely. It is clear that in workplaces there are variable ‘absence cultures’, which encourage or discourage presenteeism (Ruhle & Süß, 2019 ). PGRs too endorse the existence of different absence cultures, enacted in individual supervisory relationships and at wider lab or department levels; influencing the extent to which PGRs feel able to take absences (Berry et al., 2021a ). The current findings would suggest that absence cultures that create or reinforce reticence to take mental health-related intermission may actually increase attrition intention. It is important to consider, nonetheless, that intermission typically results in loss of income for fully-funded students, whereas absenteeism and presenteeism usually do not. Moreover, we acknowledge that current participants do not include PGRs who have discontinued their studies. Consequently, we do not know the nature of the association between having taken mental health-related intermission and later attrition. Nonetheless, intention to leave is considered one of the strongest predictors of attrition (Ertem & Gokalp, 2019 ), making it is plausible that mental health-related intermission protects against mental health-related attrition.
There are important limitations to note. The data used are cross-sectional. Therefore, regression analyses test whether associations modelled between variables are consistent with theorised directions of effects, and do not test the directionality or causality of these associations. Moreover, models tested include a large number of variables, which makes the unique contribution of individual covariates difficult to interpret (Kraha et al., 2012 ). In addition, the metric of the predictor variable influences the size of the odds ratios presented, for the odds ratio reflects the change in odds associated with a one-unit increase in the exposure (Szumilas, 2010 ). Therefore, odds ratios may be closer to one for symptoms, and psychological and social factors, because the unit of measurement is small compared to the size of any meaningful change. Whilst the Box-Tidwell test results for continuous predictor linearity were acceptable (Wuensch, 2021 ), it is possible that there was a slight degree of non-linearity that may have caused underestimation of effects of these predictors (Long, 2008 ). The risk of this with respect to depression and anxiety seems low, as these variables were significant in most models, yet it could be the case that the supervisory relationship is an even more powerful predictor of attendance behaviours than observed here.
The sample from which current data were derived is a self-selecting sample of UK PGRs and therefore, the generalisability of findings is constrained. This is a common challenge to research on PGRs, for their representation in epidemiological research is poor and they are often undifferentiated from other populations of students. We note that, overall, the sample is predominantly female, White, identified as UK citizens, and had full PhD funding in place. Efforts to engage male PhD students, those from minority ethnic backgrounds and those not of UK citizenship, without full funding, should be made to ensure greater representativeness of these groups. More specifically, the current sample reflects only current PGRs and not those who have discontinued their studies. Therefore, attrition intention and predictors thereof may actually correspond to PGRs who are less likely to actually terminate their PhD studies early. Future research should test longitudinal predictors of attendance, intermission, and attrition intention, and attrition itself. Finally, we have tested the specified predictors in these models independently, however, it seems likely that they interact. We would anticipate that psychological and social factors are mediated by their association with mental health symptoms, and that social and relational factors in addition are mediated by psychological factors, for example, supervisory relationships likely impact on PGRs’ perceived competence (Litalien & Guay, 2015 ).
There are several clear policy recommendations of the current study. Policy should mandate supervisor training regarding mental health disclosures and supporting students with the enablement of reasonable adjustments to mitigate the impact of mental health problems on their PhD engagement and attendance; supporting them with mental health-related intermission when necessary. Such training should additionally support supervisors to form effective relationships with PGRs, in which there is appropriate guidance, direction, proximity and support; whilst scaffolding the PGR to develop a sense of their own self-agency as an emerging researcher. Finally, institutions should be asked to ensure access to interventions for mental health symptoms, which are appropriate for and accessible to PGRs, and that help mitigate the impact of perfectionistic thinking. Moreover, institutions should be expected to examine their structures and processes and consider how these may promote connectedness, with the provision of social initiatives to increase social support and reduce loneliness.
This study has identified a number of risk factors for absenteeism, presenteeism, and mental health-related intermission and attrition intention among UK PGRs. The most consistent predictive associations were that sociodemographic factors and mental health problems predicted attendance problems, intermission and attrition intention. Psychological and social factors made smaller and less robust contributions to the prediction of attendance and attrition intention, yet there appeared a role for perfectionism and loneliness in greater chance of presenteeism and taking intermission. Supervisory relationship quality appeared to reduce the likelihood of absenteeism and considering PhD attrition. Current findings emphasise the need to provide appropriate prevention and intervention initiatives for PGRs with mental health problems, including enhancement of social connectedness and supervisory relationship quality. Such initiatives should have dual benefits of reducing PGR mental health problems and scaffolding positive PhD attendance and completion intention.
The dataset related to this study is available from the corresponding author upon reasonable request.
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Berry, C., Niven, J.E. & Hazell, C.M. Predictors of UK postgraduate researcher attendance behaviours and mental health-related attrition intention. Curr Psychol 42 , 30521–30534 (2023). https://doi.org/10.1007/s12144-022-04055-1
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Overall decline in number of doctoral candidates winning support masks even sharper drop among uk-domiciled applicants.
The number of PhD students winning UK research council funding has fallen sharply in recent years, according to data that shows that domestic candidates are increasingly being squeezed out by international applicants.
Figures obtained by Times Higher Education show that UK Research and Innovation funding councils supported 5,580 doctoral starters in 2021-22, down 18.4 per cent on the 2018-19 total of 6,835. Figures for 2022-23 indicate an even sharper drop – down 28.3 per cent to 4,900, although UKRI said that this tally could increase as research organisations submit further details of new recruits.
The declines are particularly steep among British students. In 2018-19, UKRI supported 4,815 doctoral candidates from the UK alone, but by 2021-22 this had fallen by 29 per cent, to 3,420. The 2022-23 figure provided, 2,840, indicates a 41 per cent drop over five years.
The shifts coincide with UKRI’s decision to extend full PhD studentships to international students, which came into effect in 2021-22. The previous year, when they were eligible for fees-only awards, 345 students from outside the UK and the European Union started publicly funded PhDs in the UK. This then tripled to 1,020 in 2021-22 and, according to the 2022-23 data, 1,055.
While international students made up 6 per cent of UKRI-backed starters prior to the policy change, they now represent 22 per cent. Meanwhile, the UK proportion has fallen from 70 per cent in 2018-19 to 58 per cent in the latest data. The share of EU students has fallen also, from 21 per cent – 1,420 students – in 2018-19, to 16 per cent – 780 students – in 2022-23.
UKRI, which funds about one in five of the estimated 105,000 doctoral students in the UK, caps the proportion of international PhD students it funds at 30 per cent.
Daniel Rathbone, deputy executive director of the Campaign for Science and Engineering, said the decline in UKRI-backed PhD starters was troubling given the UK’s ambitions to find an additional 150,000 researchers and technicians by 2030, as outlined in its 2021 R&D People and Culture Strategy .
“To meet the government’s ambitions to be more research-intensive, the UK will need a substantial increase in researchers. It is worrying, therefore, to see the number of students starting UKRI-funded PhDs has fallen in recent years,” said Dr Rathbone.
“We would urge the government to make sure that UKRI has the necessary funding for PhD studentships, and for students to be well supported during their studies.”
The data, which was provided following a Freedom of Information request, shows that the declines in the number of UK PhD students supported were particularly pronounced at certain research councils. At the Engineering and Physical Sciences Research Council, the UK total was down to 1,445 in 2021-22, and 1,150 in 2022-23, compared with the 2018-19 figure of 1,980. At the Medical Research Council, the number of UK students supported has almost halved from 325 in 2018-19 to 195 in 2021-22 and 180 last year.
The data is also likely to raise further concerns over PhD funding provided by the Arts and Humanities Research Council, which announced in September that it would cut the number of doctoral students it supports by a quarter , to 300 a year. In 2018-19, it supported 1,010, of which 660 were from the UK, while the 2022-23 data puts the overall total at 660, with 355 of these from the UK.
“Anyone who cares about the arts and humanities should be concerned about these figures,” said Andrew McRae, dean of postgraduate research at the University of Exeter .
“With only 355 places to UK arts and humanities applicants in 2022, and with more cuts to come, I wonder how long I can reasonably look talented undergraduates in the eye and encourage them to take on the additional debt repayments that will come with a master’s loan, in the hope of getting a foothold on the academic ladder.”
Professor McRae said that while he was “broadly supportive of an internationalised field of [postgraduate researchers] being supported by UKRI”, opportunities for domestic students were now “seriously constrained” and further reflection may be needed.
“We risk seeing some research areas becoming non-viable,” Professor McRae added. “I’m not seeing any coordinated thinking about the future academic staffing and research base for UK universities.”
In its recent publication, A New Deal for Postgraduate Research , UKRI said that it would keep its support for international students “under review”. In a statement, it said that 2018-19 was a “particularly high year for doctoral enrolments in the UK”.
“UKRI continues to work with the sector to monitor the rate of doctoral studentships and consider how best to invest public money that ensures students are appropriately supported and we can meet the government’s ambitions,” UKRI said.
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Doctoral students show high levels of stress in comparison to other students, and ongoing uncertainty in terms of graduate career outcomes can make matters worse.
Before the pandemic, one in five research students were expected to disengage from their PhD. Disengagement includes taking extended leave, suspending their studies or dropping out entirely.
COVID-19 has made those statistics far worse. In a recent study , 45% of PhD students surveyed reported they expected to be disengaged from their research within six months, due to the financial effects of the pandemic.
Many factors influence whether a student completes their doctorate. They include supervision support (intellectual and pastoral), peer support (colleagues, friends and family), financial stability and good mental health.
In our recently published book The Doctoral Experience Student Stories from the Creative Arts and Humanities – which we edited with contributions from PhD students – students outlined their experiences of doing a doctorate and shared some useful strategies for how to keep going, and ultimately succeed, in the doctoral journey.
Completing a doctorate involves much more than generating knowledge in a specific discipline. It is a profoundly transformational process evolving over a period of at least four years — and often longer.
This entails personal questioning , development in many areas of life, and often a quite significant personal and intellectual reorientation. The PhD brings with it high expectations, which in turn creates high emotional stakes that can both inspire and derail students. This is coupled with coming to see and think about the world very differently — which for some can be a daunting prospect, as all previously held assumptions are thrown into disarray.
Such a profoundly existential process can itself engender anxiety, depression and trauma if students are not equipped with the self-care strategies that enable resilience.
Read more: PhD completion: an evidence-based guide for students, supervisors and universities
Every chapter in our book, written by a different student, emphasises the need to engage in deep thinking and planning regarding their personal goals, strengths and weaknesses, and ways of working before starting the PhD.
This is important preparatory work to ensure any challenges that arise are surmountable.
In her chapter, Making Time (and Space) for the Journey, AK Milroy writes she learnt to
[…] analyse and break down the complicated doctoral journey into a manageable, achievable process with clear tasks and an imaginable destination.
She writes this includes involving family and friends in the process because
[…] it is paramount to ensure these people understand the work that lies ahead, and also that they too are being respected by being included in the planning.
Relationships were, above all, a critical component of the experience for many of the student writers. The supervisory relationship is the most obvious one, which Margaret Cook describes as the student undertaking a form of academic apprenticeship.
Read more: Ten types of PhD supervisor relationships – which is yours?
The student authors also identify strategies for the “thinking” part of the research process once enrolled. These include acknowledging that the free and creative element of mind-wandering and downtime are as legitimate as the focused, task-oriented work of project management, such as preparing checklists and calendars.
AK Milroy calls these “strategic side-steps”.
Peter Mackenzie, who researched regional jazz musicians, went a step further to connect with his participants.
I felt like an outsider but once I started to play with the guys on the bandstand that night at the Casino, I sensed a different level of appreciation from them. After playing and taking on some improvisations, I could feel the group relax. I was no longer an outside musician. Even better, I wasn’t seen as an academic. I was one of them.
The task of writing, of course, cannot be ignored in the long doctoral journey.
Drafting and redrafting, jettisoning ideas and arguments along the way, is acknowledged as a core component of the doctoral learning process itself, and the many attempts are not proof of failure.
Gail Pittaway writes about extending networks beyond one’s supervisors and university to collaborate with those in the discipline nationally and internationally.
This can be productive and lead to co-written articles and editing special issues of journals, which can positively influence the PhD thesis.
[…] by developing confidence in sharing ideas, seeking peer review feedback and editorial advice from a wider range of readers as some of these sections are submitted for publication, the writing of the thesis is encouraged and energised.
Many of the student authors acknowledge questioning, self-doubt and fear of the unknown are central to creating and performing research. While this might be frightening, they say it should be embraced as this is where innovation and novelty can arise.
Charmaine O'Brien writes about how transformative learning is dependent on this period of complexity and not-knowing. While “failure to make experience conform to what we already know is threatening because it destabilises a sense of how we know the world, and ourselves in it, resulting in psychological ‘dis-ease’”, staying with it – and having supportive supervisors – ensures the student becomes a doctoral-level thinker.
Read more: Mindfulness can help PhD students shift from surviving to thriving
Lisa Brummel writes of extending requirements of occupational health and safety into her own life. This takes forms such as family, friends and exercise, assisting with work-life balance and good mental health.
After all, two of the most significant resources PhD students possess to do the work required are their physical and mental capacity.
Finally, students must love their topic. Without an innate fascination for the field in which they are researching, this often tumultuous intellectual, emotional and personal journey may derail.
In the four-plus years spent doing a doctoral degree, any range of major life events can occur. Births, deaths, marriages, separations and divorces, illnesses and recovery, are all possible. Being willing to seek help and knowing who to ask can be the difference between completing and collapsing.
There is no pleasure without pain in the doctoral journey, but with the right frame of mind and supportive supervisors, the joys certainly outweigh the suffering.
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Employment and earnings of higher education graduates broken down by graduate characteristic, subject studied and university attended.
Main text: sfr60/2016.
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These experimental statistics use graduate outcome data to look at employment and earnings of higher education graduates 1, 3, 5 and 10 years after graduation.
They use the same information as the statistics published on 4 August 2016 . They concentrate on data for 2009 graduates (those who completed their degree at the end of the 2009 academic year) 1, 3 and 5 years after graduation.
These statistics provide further breakdowns by:
Complete our second survey to feedback on these experimental statistics (SFR60) .
The ‘Higher education longitudinal education outcomes experimental statistics - informal consultation: government response’ summarises the responses to the informal consultation on our first graduate outcome statistics (SFR36) and the proposed changes to future releases.
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As students prepare for the new term, the Higher Education Policy Institute is publishing a report on non-continuation (or ‘drop-out’) rates.
A short guide to non-continuation in UK universities (HEPI Policy Note 28) by Nick Hillman, HEPI’s Director, looks at the scale of the problem, including showing that the UK has the lowest drop-out rate of any OECD country. It also considers which students are most at risk of not completing their courses and what changes could usefully be implemented to reduce non-continuation rates further.
Nick Hillman, the Director of HEPI and the author of the new report, said:
Dropping out from higher education is receiving more focus than ever before. Policymakers and regulators are increasingly judging institutions by their non-continuation rates. There is even a chance that funding could be closely linked to student retention in future. It is good that people are discussing the issue in greater detail, not least because some students are more prone to dropping out than others – including Black students, part-time students, poorer students and commuter students. But it is nonetheless ridiculously easy to draw the wrong conclusions from the data. Not all instances of dropping out are bad. There can be good reasons why a student cannot or should not continue with their course. Moreover, the UK already has the lowest drop-out rate in the developed world. Cack-handed attempts to reduce this further could actually disincentivise the recruitment of disadvantaged students. It could also disrupt the Government’s own well-received plans to promote more flexible lifelong learning options. The best way to help students who are considering leaving but would benefit from completion is not heavy-handed regulation but instead to spread best practice. Among our proposals are targeted interventions for at-risk groups, measures to encourage a greater sense of belonging among students and non-stigmatising re-entry routes for those who leave but then want to start afresh. We also recommend exit interviews for early leavers, so that lessons can be learned when things do go wrong, and looking afresh at maintenance support to ensure students have enough income to live safe and stable lives.
Notes for Editors
The Higher Education Policy Institute was established in 2002 to help shape the higher education debate with evidence. It is the UK’s only independent think tank devoted to higher education. HEPI is a non-partisan charity funded by higher education institutions and other organisations that wish to see a vibrant policy debate.
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And one important similarity.
Helen Robertson
Credit: Malte Mueller/Getty
And one important similarity.
11 March 2020
Malte Mueller/Getty
In 2019, I took a risk by moving halfway around the world as a postdoctoral researcher in molecular evolution.
Since then, I’ve been struck by how different the grad school experience is here at the University of Chicago in the US, compared with my time at the University College London in the UK, where I completed my PhD in 2017.
Here’s what I’ve noticed:
In the UK, you’re likely to apply directly to a lab for an advertised project or one that you develop with your supervisor.
In the US, the application process is more centralized. You usually apply to a school instead of a lab. Some programs even require you to take a standardized Graduate Entry Program test, though this seems to be on the decline .
Grad school interviews in the US tend to be longer. They can involve a series of interviews, tours, and faculty events over a number of days.
I’ve been surprised by how all-encompassing a US doctorate can be. Even after the first year of teaching, the number of seminars, journal clubs, and university-related activities make the US PhD experience very grad school-centric.
I was fortunate during my UK-based PhD to approach it more like a full-time job than a continuation of my masters year. There were intense periods that required late nights in the lab, but I had time to pursue other interests , which provided some balance and made me more productive at work.
Of course, it’s difficult to generalize about working patterns. Demanding schedules are not wholly dictated by the country you’re studying in. A recent study found that 76% of surveyed grad students spent more than 41 hours a week on their project.
Probably the best-known difference is the time it takes to complete a PhD.
UK PhD programs tend towards three years in length, although it’s increasingly getting closer to four years – a trend that might soon be reflected in funding arrangements .
It’s a different story in the US, where, according to the Survey of Earned Doctorates , students take an average of 5.7 years to graduate.
Fees err on the more expensive side in the US, as they do for undergraduate degrees – although this isn’t always true for international students.
US PhD fees, coupled with the longer study time, means that the costs associated with grad school are generally higher than in the UK, even before living costs are considered.
If you have a funding body attached to your project, it will likely pay your tuition fees as part of its finance package. But this flags a major difference between the two countries: funding and scholarships.
From my understanding, most advertised science-based PhD projects in the UK are attached to funding, which covers tuition fees, bench costs, and living expenses. The tax-free PhD stipend set by all UK Research Councils is £15,285 (approximately US$20,000), although other funding bodies pay more.
In the US, there is no national funding level – your level of financial support will be dictated by your school or lab. This means there is generally much more encouragement for US PhD students to apply for their own funding than there is in the UK.
This is good experience for a future scientific career, but if you have to work additional hours to supplement scholarships, you’ll ultimately end up with less time for your project.
This is particularly true in the first year for US PhDs, which includes lectures, exams, and lab rotations. Only at the end of the first year, after passing your qualifying exam, do you have the opportunity to pick the lab you’re going to pursue your PhD research in.
In the UK, I started in the lab that I spent the duration of my studies in. This meant no structured classes or rotations in my first year, and I began my own research right away.
PhDs that are run through a Doctoral Training Centre (DTC) – centres that manage the Research Council-funded PhD degrees – are increasingly popular in the UK, and include classes and rotations during the first year, but often without the frequent exams and coursework that characterize grad school in the US.
Writing my thesis was the final hurdle of my UK PhD experience. It gave me the opportunity to document my ideas, successes (and failures), and the context of my project. I defended my thesis in a closed session with two examiners: one internal to my institution, and one external.
From what I’ve seen, finishing a doctorate in the US is less focused on a thesis. Instead, your committee determines that you have completed sufficient work and skill attainment to warrant your defense. Only then can you write your thesis, and defend it in a public session.
In the UK, it’s unlikely you’ll know your examiners well, but a US PhD defense is assessed by the same thesis committee that have known you for the duration of your studies.
My UK PhD funding set no teaching requirements: instead, I was free to teach labs and mark coursework at the discretion of my supervisor. And I was paid for any teaching hours I did.
Teaching requirements in the US vary from school to school. For some students, working as a teaching assistant is necessary to pay fees and living expenses – particularly if you don’t have comprehensive funding.
There might also be minimum teaching requirements for the duration of your PhD in the US. In this respect, the time commitment and financial compensation of teaching is very institution-specific.
Despite the differences in structure and requirements between UK and US PhDs, one thing that is common to them all is that, ultimately, your PhD is going to be shaped by the lab you decide to join.
If have a positive working environment and appropriate guidance and support from your supervisor, and you’re interested in and motivated by your thesis topic, then your grad school experience will likely be rewarding.
And that’s true regardless of the country you’re studying in.
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Aside from the fact that doing a Ph.D. seems to put you at a greater risk of being anxious or depressed than other occupations, some students may also face the question: will I ever finish my thesis at all ? This post digs into research about doctoral attrition and completion, and what factors seem to make dropping out more likely. Do not give up!… unless you really want to.
Image by Siggy Nowak from Pixabay
About 10 years ago, when I left my job in the telecom industry to pursue an academic Ph.D., I started pursuing my other secret dream: being a psychologist. I enrolled in an online masters program on Psychology research. However, several months into the program, it was clear that this was not going to be a piece of cake. I was behind on the readings, some of the concepts in the courses were incomprehensible to me (not surprising, since my background was in Engineering), and I had some unpleasant online interactions with my peers when seeking help about these issues. My morale started to falter, and I started wondering: should I cut my losses and focus on my other main project (the doctoral degree that I had started in parallel)? Or was it better to drop out of the Ph.D. and pursue the shorter, maybe more manageable masters degree?
If you ever faced this kind of thoughts, you are not alone. Not at all . In the research literature about students dropping out of doctoral programs (or “attrition”, as they call it), very often the ballpark of 40–60% attrition rate is mentioned 1 . Imagine you are in a classroom with your peer Ph.D. students. Look to the person on your left. Look to the person on your right. According to the statistics, only one or two of you will ever finish the Ph.D.
That’s a hard pill to swallow.
Of course, this is just a general approximation. The numbers vary quite a bit from university to university, and across the different disciplines: in one study, students in science and technology were 50% more likely to complete their Ph.D.’s than health sciences ones, and more than twice as likely compared to doctoral students in the humanities and social sciences 2 . This is probably due to many social, economic and cultural factors that are quite different in each discipline (rather than the inherent difficulty of the subject). Furthermore, even getting to these numbers is quite hard, since very often the researchers running these studies (and the doctoral programs themselves) don’t have a good way to know if a student has actually dropped out, or is just unusually quiet.
There is also the issue of when will you drop out. Several studies mention that dropping out of a Ph.D. is more probable in the first two years 1 , 2 . This is probably due to the students coming to the doctorate with a certain image or expectation of what doing research looks like, and academic research life not living up to those expectations – leading to disillusionment and dropout. To avoid this, in certain areas like biomedical research, students spend some time at the beginning of the doctorate rotating around different labs to get a better sense of what working in research looks like… only sometimes this also backfires, when labs start competing fiercely for the best students, so that some labs show a “friendly façade” during rotation, and a much harsher reality once the student incorporates to the lab for real 3 .
So, it is clear now: if about half of the doctoral students actually drop out of the Ph.D., probably many more have at least considered quitting. Indeed, I’d wager that you are quite lucky if you have not thought of abandoning the Ph.D. so far.
Now that we know this is a quite common problem, what are the factors related to greater chances of dropping out (or persisting until completion)? Looking into the research on this issue, I found five factors that appear quite often 4 :
OK, so far things seem logical. If we are alone, we are not academic over-achievers, or we have to get an unrelated job to make ends meet, we will generally have a harder time during the dissertation – and more chances of being faced with the dilemma of abandoning it.
But… what is the right answer?
Most of the research I’ve seen around this topic describes dropping out as a big problem, a waste of time and resources for everyone involved (students, supervisors, universities, society). And, don’t get me wrong, I totally see how it is a problem that should not be dismissed lightly. However, I cannot help but think that we are seeing only one side of the coin: that of the institutional success, and the student as a human resource . We could also be a bit more empathetic and look at students as a human beings , and their experience: what if dropping out is the better option for this particular student, as a person, at this point in time? In one paper, a doctoral student explains:
‘‘I discussed withdrawing with family and my significant other; they just wanted me to be happy and, given the treatment that I received [from my advisor] for months, it seemed like the clear choice’’ 3
The quote reads like a really well thought-out, meditated decision, after enduring a toxic situation – regardless of the resources “wasted”.
Plus, are they really wasted? We may be forgetting that, even if you do not have a paper calling you “Ph.D.”, it is quite probable that you learned a few useful things during this journey, however incomplete: you learned to read scientific papers, you learned how your kind of research is really made, you learned to write and to argue a bit better, and probably you also practiced your critical thinking (which seems in short supply these days). I wouldn’t call that a total waste.
So yes, you should consider carefully before starting a Ph.D. (or accepting to supervise one). But, if the decision was made in good faith, forget about the funding, forget about the time “wasted”… they are sunk costs 7 . Rather, ask yourself: am I (or is this person) going to be an effective, convinced, purposeful researcher, if I continue my doctoral training under these conditions and in this place? If the answer is no, then maybe quitting isn’t a such bad idea. Heck, there is even research that suggests that, if you are at the point where you could decide by tossing a coin, you would be better off making the change right away! 8
If you are facing this conundrum, evaluate your environment and your daily experience carefully, and talk about it with family and close friends. But the decision is only yours. Yet, I can give you a general rule of thumb, from what I’ve seen in the academic world so far: if you think you are not “smart enough”, or you have any other argument for why you will never succeed at this that smells even remotely of impostor syndrome , I’d say you can make it (believe me, I’ve seen some really un-smart people get doctorates). If, on the other hand, your lab environment is toxic, your economic or social situation is really bad, or you feel deeply unhappy every day you do research, maybe it is time for a re-evaluation.
You can do it, if you want to endure (or -gasp!- enjoy) the process.
Coming back to my own personal case, I did drop out of the Psychology masters, to focus on my Ph.D. And I don’t regret it one bit. Indeed, even after focusing my attention on the Ph.D., a researcher could have told me that my chances were still not terribly optimistic: I was single, I was completely self-funded, my masters grades were not exactly glowing, and I had no idea whether the doctorate would bring me incredible job opportunities.
Oddly enough, not only I managed to finish my Ph.D.; I actually consider that year one of the happiest, most fulfilling of my life.
Am I an outlier? Maybe yes. Was I extremely lucky? Probably so. However, in some of the latest readings I did for this post, I found an alternative, reasonable explanation. But this post has gotten quite long already. You can find out more about this other strand of doctoral education research, in the next post of the series on doctoral dropout .
Have you ever considered dropping out of your Ph.D.? can you think of other factors that made you stay (or abandon it)? Do you think there is a right moment to quit the doctorate? Let me know in the comments section below!
Common problems and challenges in doing the PhD, from mental health (e.g., depression or anxiety) or productivity challenges , to writing or dropping out of your PhD .
Mental health and wellbeing tips and advice : common mental health symptoms in the PhD , tips to avoid dropping out of the doctorate , the importance of sleep , holidays or advice from positive psychology to keep yourself motivated during the PhD.
PhD productivity tips and advice : from the classic Pomodoro technique , to avoiding to-do list overwhelm , dealing with multiple projects and priorities , staying productive and motivated , how I manage my daily tasks or how I do my weekly reviews .
PhD-specific tools , like the CQOCE diagram to conceptualize your PhD, the NABC method to structure your research presentations, or the process I use to write scientific papers or make big career decisions .
Supervision tips and advice , about giving feedback on student papers , or supporting a sense of progress in your doctoral students .
See, for example, Bair, C. R., & Haworth, J. G. (2004). Doctoral student attrition and persistence: A meta-synthesis of research. In Higher education: Handbook of theory and research (pp. 481–534). Springer. ↩︎
Wollast, R., Boudrenghien, G., Van der Linden, N., Galand, B., Roland, N., Devos, C., … Frenay, M. (2018). Who Are the Doctoral Students Who Drop Out? Factors Associated with the Rate of Doctoral Degree Completion in Universities. International Journal of Higher Education , 7 (4), 143–156. ↩︎
Maher, M. A., Wofford, A. M., Roksa, J., & Feldon, D. F. (2017). Exploring Early Exits: Doctoral Attrition in the Biomedical Sciences. Journal of College Student Retention: Research, Theory & Practice . https://doi.org/10.1177/1521025117736871 ↩︎
Please be aware that most of this evidence is from correlational studies, so it is hard to know if these factors are the causes of the dropout, or (more probably) symptoms of a different underlying cause (or causes). ↩︎
Rigler Jr, K. L., Bowlin, L. K., Sweat, K., Watts, S., & Throne, R. (2017). Agency, Socialization, and Support: A Critical Review of Doctoral Student Attrition. Paper Presented at the 3rd International Conference on Doctoral Education . Presented at the University of Central Florida. Retrieved from https://files.eric.ed.gov/fulltext/ED580853.pdf ↩︎
Gardner, S. K., & Gopaul, B. (2012). The part-time doctoral student experience. International Journal of Doctoral Studies , 7 (12), 63–78. Retrieved from http://informingscience.com/ijds/Volume7/IJDSv7p063-078Gardner352.pdf ↩︎
Arkes, H. R., & Blumer, C. (1985). The psychology of sunk cost. Organizational Behavior and Human Decision Processes , 35 (1), 124–140. ↩︎
Levitt, S. D. (2016). Heads or tails: The impact of a coin toss on major life decisions and subsequent happiness (Working Paper No. 22487). Retrieved from National Bureau of Economic Research website: https://www.nber.org/papers/w22487 ↩︎
Luis P. is a Ramón y Cajal research fellow at the University of Valladolid (Spain), investigating learning technologies, especially learning analytics. He is also an avid learner about doctoral education and supervision, and he's the main author at the A Happy PhD blog.
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Published statistics, september 2024.
| Official | 3 September 2024 |
Information about the size and shape of provision delivered by each provider, updated to include the latest student data up to the 2022-23 academic year. This publication is intended to support understanding of a provider’s context in terms of its size, the types of courses it offers, the mix of subjects it offers and the characteristics of its students.
| Official | 3 September 2024 |
An update to our student characteristics data to include:
| Official | 3 September 2024 |
This provides a sector-level summary of responses to the Sexual misconduct prevalence survey pilot 2023, aggregated across the 12 providers who took part.
| Ad hoc | 31 July 2024 |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. This is a scheduled update to the data relating to academic year 2023-24 that was originally published on 28 March 2024.
| Official | 26 July 2024 |
Annual release of the student outcome measures that we construct for all OfS-registered providers for the purposes of regulating student outcomes. This publication provides the indicators and split indicators we construct at individual provider level in respect of continuation, completion and progression outcomes. Continuation and completion outcomes are updated to include the latest student data up to the 2022-23 academic year. Progression outcomes incorporate information for graduates in the 2021-22 academic year. Published as a visual and interactive dashboard of data, supported by accompanying data tables, it covers undergraduate and postgraduate students in each combination of mode and level of study.
| Official | 25 July 2024 |
Annual release of distributions of the indicator, benchmark and difference from benchmark values calculated in respect of student outcomes and experience measures across all OfS-registered providers. This publication provides sector distributions for each measure across different modes and levels of study, and for various student and course characteristics. Continuation and completion outcomes are updated to include the latest student data up to the 2022-23 academic year. Progression outcomes incorporate information for graduates in the 2021-22 academic year. Student experience measures incorporate information for final year undergraduates in 2022-23 and 2023-24.
| Official | 25 July 2024 |
Annual release of data indicators that provide a sector- and provider-level picture of the patterns in access and participation across the student lifecycle. The access, continuation, completion and degree outcome measures are updated to include the latest student data up to the 2022-23 academic year. Progression outcomes incorporate information for graduates in the 2021-22 academic year. Published for all OfS registered providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 25 July 2024 |
Update of the student outcome and experience measures that we construct for all OfS-registered English providers for the purposes of making assessments through the Teaching Excellence Framework (TEF). This publication updates the indicators and split indicators we constructed at individual provider level in respect of continuation, completion and progression outcomes and student experiences to inform TEF 2023 assessments. Continuation and completion outcomes are updated to include the latest student data up to the 2022-23 academic year. Progression outcomes incorporate information for graduates in the 2021-22 academic year. Student experience measures incorporate information for final year undergraduates in 2022-23 and 2023-24. Published for all OfS registered providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 25 July 2024 |
Statistics on students’ views about their courses from the National Student Survey (NSS) 2024.
| Official | 10 July 2024 |
Official statistic summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
| Official | 27 June 2024 |
Data on the number of full-time equivalent higher education students in the academic year 2022-23 at providers which are registered with the Office for Students. This publication is updated every four weeks to include providers that have successfully registered since the last publication.
| Official | 26 June 2024 |
Summary statistics on data collected on providers’ implementation of three areas of the Prevent duty: the reporting and management of Prevent-related welfare cases, the consideration and approval of external speakers and events, and the delivery of Prevent-related training to staff. This will be a release of sector-level data. The previous data release was May 2023.
| Official | 4 June 2024 |
This report contributes to the overall evidence base for participation in higher education in the areas targeted by Uni Connect. It includes the cohort of learners applying to higher education courses in the 2022 UCAS application cycle.
| Ad hoc | 15 May 2024 |
Publication | Type of statistic | Publication date |
Annual release of HESES survey data These figures are submitted to the OfS by individual higher education providers, and give an early indication of the number of higher education students studying in each academic year. They include estimates of student activity for the full academic year and are used primarily to inform the allocation of teaching funding for the following academic year.
| Official | 28 March 2024 |
Annual release of UK-wide confirmed intakes for 2022-23 and the initial intakes for 2023-24.
| Official | 7 December 2023 |
Statistics on students' views about their courses, comparing across different student groups. From the 2023 National Student Survey.
| Official | 9 November 2023 |
Publication | Type of statistic | Publication date |
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In this report, we explore data relating to students who have reported to their university or college that they have a mental health condition. We consider their outcomes, and the makeup of this group of students in terms of a small set of other characteristics and compare this with those who have not reported that they have a mental health condition.
| Ad hoc | 19 October 2023 |
An update to the following KPMs to reflect the most recent data: | Official | 9 October 2023 |
Publication | Type of statistic | Publication date |
---|---|---|
An update to postcodes included in using the May 2023 ONS postcode products.
| Official | 28 September 2023 |
Publication | Type of statistic | Publication date |
---|---|---|
Statistics on students’ views about their courses from the National Student Survey.
| Official | 10 August 2023 |
An update to the following KPMs to reflect the most recent data:
| Official | 8 August 2023 |
Publication | Type of statistic | Publication date |
---|---|---|
An update to our student characteristics data to include:
| Official | 25 July 2023 |
An update to our report on analysis of degree classifications over time, which will include individuals who graduated in the 2021-22 academic year.
| Official | 20 July 2023 |
Updated progression indicators to incorporate information for graduates in the 2020-21 academic year. Published for all OfS registered providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 6 July 2023 |
Updated progression indicators to incorporate information for graduates in the 2020-21 academic year. Published for all OfS-registered providers for the purposes of regulating student outcomes.
| Official | 6 July 2023 |
Updated sector distributions of progression outcomes to incorporate information for graduates in the 2020-21 academic year. Distributions of the indicator, benchmark and difference from benchmark values calculated in respect of progression outcomes across all OfS-registered providers.
| Official | 6 July 2023 |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. This is a scheduled update to the data that was originally published on 22 March 2023.
| Official | 6 July 2023 |
Publication | Type of statistic | Publication date |
---|---|---|
Summary statistics on data collected on providers’ implementation of three areas of the Prevent duty: the reporting and management of Prevent-related welfare cases, the consideration and approval of external speakers and events, and the delivery of Prevent-related training to staff.
| Official | 25 May 2023 |
Official statistic summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
| Official | 25 May 2023 |
Publication | Type of statistic | Publication date |
---|---|---|
The student outcome measures that we construct for all OfS-registered providers for the purposes of regulating student outcomes. This publication provides the indicators and split indicators we construct at individual provider level in respect of continuation, completion and progression outcomes, with continuation and completion outcomes updated to include the latest student data up to the 2021-22 academic year. Published as a visual and interactive dashboard of data, supported by accompanying data tables, it covers undergraduate and postgraduate students in each combination of mode and level of study.
| Official | 12 April 2023 |
Distributions of the indicator, benchmark and difference from benchmark values calculated in respect of student outcomes and experience measures across all OfS-registered providers. This publication provides sector distributions for each measure across different modes and levels of study, and for various student and course characteristics, with continuation and completion outcomes updated to include the latest student data up to the 2021-22 academic year.
| Official | 12 April 2023 |
Information about the size and shape of provision delivered by each provider, updated to include the latest student data up to the 2021-22 academic year. This publication is intended to support understanding of a provider’s context in terms of its size, the types of courses it offers, the mix of subjects it offers and the characteristics of its students.
| Official | 12 April 2023 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector- and provider-level picture of the patterns in access and participation across the student lifecycle updated to include the latest student data up to the 2021-22 academic year and to account for outcomes of our recent consultation on constructing student outcome and experience measures. Published for all OfS registered providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 28 March 2023 |
We updated the following KPMs to reflect the most recent data: * *We also published two new KPMs on , and *.
| Official and *Experimental | 23 March 2023 |
These figures are submitted to the OfS by individual higher education providers, and give an early indication of the number of higher education students studying in each academic year. They include estimates of student activity for the full academic year and are used primarily to inform the allocation of teaching funding for the following academic year.
| Official | 22 March 2023 |
Statistics on students’ views about their courses from the . This is a scheduled update to the data that was originally published on 6 July 2022. This third quarterly update includes data amendments and accounts for provider mergers since the data was last published in September 2022.
| Official | 22 March 2023 |
Publication | Type of statistic | Publication date |
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2021-22 student numbers The first release of data on the number of full-time equivalent higher education students at providers which are registered with the Office for Students for the academic year 2021-22. This publication will be updated every four weeks to include providers that have successfully registered since the last publication.
| Official | 8 February 2023 |
Publication | Type of statistic | Publication date |
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We published the UK-wide confirmed intakes for 2021-22 and the initial intakes for 2022-23.
| Official | 16 December 2022 |
Publication | Type of statistic | Publication date |
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An update to our student characteristics data to include continuation, completion, attainment and progression data for each of the student characteristics. A new dashboard containing continuation, completion, attainment and progression data by entry qualifications and subject of study for the whole sector is also included.
| Official | 11 November 2022 |
We have updated our KPMs to make sure they are appropriate for our strategy 2022-25. We published KPMs on student outcomes, access to higher education and student choice.
| Official | 3 November 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year.
| Official | 28 October 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
An update to postcodes included in our using the May 2022 ONS postcode products.
| Official | 30 September 2022 |
The student outcome measures that we construct for all OfS-registered English providers for the purposes of regulating student outcomes. This publication provides the indicators and split indicators we construct at individual provider level in respect of continuation, completion and progression outcomes. It covers undergraduate and postgraduate students in each combination of mode and level of study. The dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 30 September 2022 |
The student outcome and experience measures that we construct for all OfS-registered English providers for the purposes of making assessments through the TEF. This publication provides the indicators and split indicators we construct at individual provider level in respect of continuation, completion and progression outcomes and student experiences. It covers undergraduate students in each mode of study. The dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 30 September 2022 |
Information about the size and shape of provision delivered by each provider. This publication is intended to support understanding of a provider’s context in terms of its size, the types of courses it offers, the mix of subjects it offers and the characteristics of its students.
| Official | 30 September 2022 |
Distributions of the indicator, benchmark and difference from benchmark values calculated in respect of student outcomes and experience measures across all OfS-registered English providers. This publication provides sector distributions for each measure across different modes and levels of study, and for various student and course characteristics.
| Official | 30 September 2022 |
An update to our geographical analysis of graduates who are in highly skilled employment. In this report we update the highly skilled employment measure for undergraduate qualifiers, and include an additional measure for postgraduate qualifiers.
| Official | 30 September 2022 |
An update to our set of measures that seeks a better understanding of how outcomes vary for groups of students holding different sets of characteristics. This update includes seven ABCS measures, which encompass access, continuation, completion and progression stages of the student lifecycle.
| Official | 30 September 2022 |
Statistics on students’ views about their courses from the 2022 National Student Survey. This is a scheduled update to the data that was originally published on 6 July 2022. This first quarterly update includes data amendments and accounts for provider mergers since the data was first published.
| Official | 28 September |
Data on access and participation investment, as reported by providers to support monitoring of 2020-21 access and participation plans.
| Official | 22 September 2022 |
We have updated our KPMs to make sure they are appropriate for our strategy 2022-25. We published KPMs on assessments and awards, students’ views on aspects of quality, degree attainment by ethnicity, value for money and efficient regulation.
| Official | 8 September 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. We published an update to data for the 2021-22 academic year for providers in the Approved (fee cap) category of the OfS Register.
| Official | 15 July 2022 |
Summary statistics on data collected on providers’ implementation of three areas of the Prevent duty: the reporting and management of Prevent-related welfare cases, the consideration and approval of external speakers and events, and the delivery of Prevent-related training to staff. This is a release of sector-level data. The previous data release was September 2021.
| Official | 14 July 2022 |
Statistics on students’ views about their courses from the National Student Survey.
| Official | 6 July 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
A summary of the overall financial position of universities and other higher education providers registered with the OfS (excluding further education colleges).
| Official | 30 June 2022 |
Official statistic summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology. This release includes an analysis of the impact of the COVID-19 pandemic on sector aggregate costs by activity, as an experimental official statistic.
: We delayed the publication of this statistic by two days. This statistic references important contextual data from the Annual Financial Return 2021. We have decided therefore to align the timing of these publications. | Official | 30 June 2022 |
Statistics on students’ views about their courses from the 2021 National Student Survey. This was a scheduled update to the data that was originally published on 15 July 2021. This final quarterly update includes data amendments and accounts for provider mergers since the data was last published in March 2022.
| Official | 15 June 2022 |
Annual update to the equality and diversity data for students in higher education at English higher education providers.
| Official | 7 June 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
An updated analysis of young participation in higher education in England in the areas targeted by Uni Connect, based on linked administrative data for cohorts up to and including the UCAS 2021 application cycle.
| Official | 26 May 2022 |
An update of the data analysis of the association between unconditional offers and entry to and continuation in higher education.
| Official | 19 May 2022 |
An update to our analysis of degree classifications over time, which will include individuals who graduated in the 2019-20 and 2020-21 academic years. This release has been delayed from autumn 2021 to allow us to include data for the academic year 2020-21 and improve our modelling of unexplained attainment during the pandemic.
| Official | 12 May 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
This update is to the measure on complying with enhanced monitoring requirements. | Experimental official | 8 April 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables. A separate report summarising the patterns at sector level has also been published.
| Official | 24 March 2022 |
These updates will provide the latest data for key performance measures 1, 2, 3, 4, 5, 7, 8, 9 and 18. | Official | 24 March 2022 |
Statistics on students’ views about their courses from the 2021 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2021. This third quarterly update includes data amendments and accounts for provider mergers since the data was last published in December 2021.
| Official | 16 March 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
A look at the geographical distribution of higher education teaching provision in England for 2019-20. This includes provider locations at various geographic areas, and a breakdown of subject provision. Also includes the breakdown of characteristic groups studying higher education in each region.
| Experimental official | 17 February 2022 |
The first release of data on the number of full-time equivalent higher education students at providers which are registered with the Office for Students for the academic year 2020-21. This publication will be updated every four weeks to include providers that have successfully registered since the last publication.
| Official | 16 February 2022 |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. We published data for the 2021-22 academic year for providers in the Approved (fee cap) category of the OfS Register.
| Official | 9 February 2022 |
Publication | Type of statistic | Publication date |
---|---|---|
Statistical modelling of sector level differences in rates of continuation, completion and progression for various populations of students at English higher education providers. This publication supports OfS consultations on regulating student outcomes and uses modelling to indicate the extent to which observed differences in outcomes between student groups across various student characteristics reflect other underlying factors which vary across student groups.
| Experimental | 20 January |
Distributions of the values calculated in respect of proposed student outcomes and experience measures across OfS-registered English providers. This publication supports OfS consultations on regulating student outcomes, the Teaching Excellence Framework and the data informing these functions. It provides data about each measure across different modes and levels of study, and for various student and course characteristics. Providers are anonymised in these distributions.
| Experimental | 20 January |
Analysis comparing the results calculated for OfS-registered English providers by two proposed methods for constructing completion measures. This publication supports OfS consultations on regulating student outcomes, the Teaching Excellence Framework and the data informing these functions. It provides comparisons of the measures at various points in time. Providers are anonymised in these comparisons.
| Ad hoc | 20 January |
Publication | Type of statistic | Publication date |
---|---|---|
Statistics on students’ views about their courses from the 2021 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2021. This second quarterly update includes data amendments and accounts for provider mergers since the data was last published in September 2021.
| Official | 15 December |
KPM 19 measures students who believe university provides good value for money. This update will include data for 2020 and 2021. | Official | 14 December |
Experimental contextual data summarising the impact of the COVID-19 pandemic on sector aggregate costs by activity based on estimated data collected as supplementary information from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
| Ad hoc experimental | 10 December |
We will publish the UK-wide confirmed intakes for 2020-21 and the initial intakes for 2021-22.
| Official | 7 December |
Publication | Type of statistic | Publication date |
---|---|---|
An updated analysis of the number and proportion of students who change course at the same or different provider, and whether they carried credit with them.
| Official | 30 November |
Sector-level data on investment and progress against targets, as reported by providers through the 2019-20 access and participation plan monitoring process.
| Official | 25 November |
KPM 26 monitors our progress in minimising the regulatory burden we place on providers. | Official | 16 November |
Updated experimental statistics which use earnings and employment data to put each local area into one of five groups to reflect differences in earnings and highly skilled employment in different parts of the UK.
| Official | 4 November |
Publication | Type of statistic | Publication date |
---|---|---|
An update to our set of measures that can be used to identify student groups that have different rates of continuation within, and access to, higher education.
| Official | 13 October |
Statistics comparing measures of graduate wellbeing with corresponding measures of general population wellbeing. | Official | 13 October |
Publication | Type of statistic | Publication date |
---|---|---|
An update to postcodes included in our using the May 2021 ONS postcode products.
| Official | 30 September |
Statistics on students’ views about their courses from the 2021 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2021. This first quarterly update includes data amendments and accounts for provider mergers since the data was first published.
| Official | 29 September |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. This is a scheduled update to the data that was originally published on 9 February 2021. This update includes data amendments and accounts for providers who have registered in the Approved (fee cap) category since the data was originally published.
| Official | 21 September |
An ad hoc publication showing the number of UK-domiciled 18 year-olds placed at providers in England four weeks after the Joint Council for Qualifications (JCQ) results day. The statistics include data for the most recent five years at the same time point. The gaps in entry rates between those from areas with the lowest and highest levels of young participation are described. Data is displayed by tariff groupings, based on the grouping used in our KPM2 measure.
| Ad hoc | 14 September |
An update to key performance measure 10, which will include data from NSS 2021. This measure shows how many students responded positively to the NSS question on overall satisfaction. | Official | 9 September |
As part of our monitoring, we require providers to report to us on their Prevent-related activities, including their management of welfare cases, external speakers and events, and staff training. This report will include aggregate data for the providers subject to OfS monitoring of the Prevent duty collected through the Prevent accountability and data returns (ADRs) for academic years 2017-18, 2018-19 and 2019-20.
| Official | 2 September |
Publication | Type of statistic | Publication date |
---|---|---|
Official statistic summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
Contextual data summarising the impact of the COVID-19 pandemic on sector aggregate costs by activity will be released as an ad hoc experimental statistic later in summer 2021. | Official | 29 July |
Publication | Type of statistic | Publication date |
---|---|---|
Statistics on students’ views about their courses from the National Student Survey.
| Official | 15 July |
Statistics on students’ views about their courses from the 2020 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2020. This fourth quarterly update includes data amendments and accounts for provider mergers since the data was last published.
| Official | 9 June |
Update to the equality and diversity data for students in higher education at English higher education providers. This year's update will include the addition of new and experimental characteristics.
| Official | 8 June |
Experimental statistics which use earnings and employment data to put each local area into one of five groups to reflect differences in earnings and highly-skilled employment in different parts of the UK.
| Experimental | 2 June |
Publication | Type of statistic | Publication date |
---|---|---|
An analysis of young participation in higher education in England in the areas targeted by Uni Connect, based on linked administrative data for recent cohorts up to the one that was in year 11 in 2017. Uni Connect was formerly known as the National Collaborative Outreach Programme.
| Experimental | 20 May |
An update to the experimental data released in December 2020 for cohorts of full-time first degree students. It incorporates the latest student data and some minor methodological changes to accommodate feedback received on the measure. The data combines projected completion rates with graduate outcomes data to create projected rates of progression from entry to professional employment or further study, and is published at named provider level for the first time.
| Experimental | 19 May |
KPM 26: Regulatory burden - assessing the impact of our regulatory activities | Official | 18 May |
This document contains numbers of UK-domiciled undergraduate entrants to English higher education providers between 2006-07 and 2019-20 by age, level of study, provider tariff group and markers of underrepresentation.
| Official | 10 May |
Publication | Type of statistic | Publication date |
---|---|---|
Statistics on students’ views about their courses from the 2020 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2020. This third quarterly update includes data amendments and accounts for provider mergers since the data was last published.
| Official | 17 March |
These updates will provide the latest data for key performance measures 1, 2, 3, 4, 5, 7, 8, 9 and 18. | Official | 11 March |
This is an update to TUNDRA, the area-based classification of young participation rates. The update will add the most recent available data and 2012 to 2016 data from the National Pupil Database.
| Official | 11 March |
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables. A separate report summarising the patterns at sector level will also be published.
| Official | 11 March |
KPM 16: Employers think that graduates are equipped with the required skills and knowledge. | Official | 11 March |
Publication | Type of statistic | Publication date |
---|---|---|
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. We have published data for the 2020-21 academic year for providers in the Approved (fee cap) category of the OfS Register.
| Official | 9 February |
The first release of data on the number of full-time equivalent higher education students at providers which are registered with the Office for Students for the academic year 2019-20. This publication will be updated every four weeks to include providers that have successfully registered since the last publication.
| Official | 10 February |
Publication | Type of statistic | Publication date |
---|---|---|
KPM 17: Measures of graduate wellbeing
| Official | 13 January |
Data on applications, offers, acceptances and registrations collected during the transparency return 2019.
| Ad hoc | 8 January |
Publication | Type of statistic | Publication date |
---|---|---|
An ad hoc research report containing experimental data for cohorts of full-time first degree students. The data combines projected completion rates with graduate outcomes data to create projected rates of progression from entry to professional employment or further study. Providers are anonymised in this research report.
| Ad hoc | 18 December 2020 |
Confirmed medical and dental student intake numbers from the medical and dental students survey.
| Official | 17 December 2020 |
Statistics on students’ views about their courses from the 2020 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2020. This second quarterly update includes data amendments and accounts for provider mergers since the data was last published.
| Official | 16 December 2020 |
An ad hoc publication containing access and continuation data for full-time (or apprenticeship) UK-domiciled undergraduate entrants split by ethnicity and provider tariff group. Continuation data is further split by subject group (STEM and non-STEM).
| Ad hoc | 16 December 2020 |
An analysis on the number and proportion of students who change course at the same or different provider, and whether they carried credit with them. The analysis also shows these proportions by student characteristics.
| Ad hoc | 8 December 2020 |
An analysis of the personal wellbeing of graduates 15 months after completion of their course. Graduates are grouped according to the mode and level they studied at and where they were originally domiciled, and the differences in wellbeing between these groups are explored. Results are also broken down by various personal characteristics and by the activities the graduates are undertaking at the time of survey. The data is taken from HESA’s new Graduate Outcomes survey.
| Official | 8 December 2020 |
KPM 8: Diversity of provision KPM 9: Geographical changes in availability of particular type of higher education provision
| Official | 8 December 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
An update to the analysis we published in June looking at the impacts of care experience, free school meal eligibility, parental higher education, sexual orientation and socio-economic background on outcomes in higher education. The newest release will add new characteristics to the analysis.
| Ad hoc | 26 November 2020 |
KPM 7: Ratio of outcomes achieved through access and participation to money spent on access and participation
| Official | 26 November 2020 |
An update to our set of experimental measures that can be used to identify student groups that have different rates of continuation within, and access to, higher education. The update includes a new measure for part-time continuation and the addition of some extra characteristics for the full-time continuation measure.
| Official | 26 November 2020 |
KPM 19: Student and key stakeholder perceptions of value for money. | Official | 25 November 2020 |
An update to the analysis presented in the report Analysis of degree classifications over time: changes in graduate attainment (published December 2018), which will include individuals who graduated in the most recent academic years.
| Official | 19 November 2020 |
An aggregated, sector-level version of the indicators that have contributed to the Office for Students’ (OfS) assessment of registration condition B3 for the purpose of initial registration of individual providers.
| Official | 17 November 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
Analysis of overall satisfaction rates in the National Student Survey at sector level. Responses will be split by the following student characteristics: age, gender, ethnicity, disability status, subject, and mode of study. Benchmarks for each of these characteristics will be calculated using the remaining five.
| Official | 27 October 2020 |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. This is a scheduled update to the data that was originally published on 11 February 2020. This update includes data amendments and accounts for providers who have registered in the approved (fee cap) category since the data was originally published.
| Official | 23 October 2020 |
This report summarises the characteristics of UK-domiciled postgraduate research (PGR) students at English higher education providers with high average tariff scores from 2010-11 to 2017-18.
| Ad hoc | 22 October 2020 |
Statistics on students’ views about their courses from the 2020 National Student Survey. This is a scheduled update to the data that was originally published on 15 July 2020. This first quarterly update includes data amendments and accounts for provider mergers since the data was originally published. The next update will be in December 2020.
| Official | 14 October 2020 |
Statistics on students’ views about their courses from the 2019 National Student Survey. This is a scheduled update to the data that was originally published on 3 July 2019. This last update includes data amendments and accounts for provider mergers since the data was last published.
| Official | 14 October 2020 |
An ad-hoc publication showing the number of UK-domiciled 18-year-olds placed at providers in England four weeks after the Joint Council for Qualifications (JCQ) results day. The statistics include data for the most recent five years at the same time point. The gaps in entry rates between those from areas with the lowest and highest levels of young participation are described. The publication includes data for providers that are included in our KPM 2 measure (higher tariff) and for providers that are included in our KPM 1 measure (all providers).
| Ad hoc | 2 October 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
An update to postcodes included in our using the May 2020 ONS postcode products.
| Official | 24 September 2020 |
The TUNDRA classification based on Lower-layer Super Output Areas (LSOAs), with supporting analysis.
The TUNDRA classification based on Middle-layer Super Output Areas (MSOAs), reflecting small changes in methodology
| Ad hoc | 24 September 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
The Higher Education Students Early Statistics (HESES) and Higher Education in Further Education Student (HEIFES) surveys give an early indication of the number of higher education students studying in each academic year. They are used primarily to inform the allocation of teaching funding for the following academic year. This is the fourth scheduled revision to the data that was originally published on 5 February 2019. It will include data for providers that have both registered with the Office for Students in the Approved (fee cap) category and signed off their HESES18 or HEIFES18 data by 1 July 2020. under the 'Results from previous surveys' section. | Official | 29 July 2020 |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. This is the first scheduled revision to the data that was originally published on 11 February 2020. We have published data for the 2019-20 academic year for providers in the Approved (fee cap) category of the OfS Register.
| Official | 29 July 2020 |
An update of the data analysis of the association between unconditional offers and entry to and continuation in higher education. This update includes access for English 18-year-olds who entered higher education in 2019-20 and continuation for those who entered higher education in 2017-18.
| Official | 23 July 2020 |
An ad-hoc publication showing the proportion of postcodes in parliamentary constituencies in England that are in low young higher education participation areas, defined by POLAR4 quintiles 1 and 2.
| Ad hoc | 20 July 2020 |
Statistics on students’ views about their courses from the National Student Survey, including the update of OfS key performance measure 10. This statistic was delayed by two weeks from the original publication date of 1 July 2020. The additional time allowed us to complete a full assessment of the possible impact of the coronavirus (COVID-19) pandemic on the data. We published this analysis alongside the statistic.
| Official | 15 July 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
Data summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
| Official | 19 June 2020 |
An investigation of differences in continuation rates, attainment rate and progression rates by the following characteristics:
| Ad hoc | 4 June 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
A short report exploring the characteristics of those starting Level 6 and 7 apprenticeships and where and what they are studying.
| Official | 13 May 2020 |
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables.
| Official | 7 May 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
An update to the following key performance measures (KPMs):
| Official | 2 April 2020 |
An update to the following key performance measures (KPMs):
| Official | 2 April 2020 |
Data for entrants, enrolled students and qualifying students, by equality and diversity characteristics.
| Official | 2 April 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
Statistics on students’ views about their courses from the 2019 National Student Survey. This is a scheduled update to the data that was originally published on 3 July 2019. This update includes data amendments and accounts for provider mergers since the data was originally published. The next update will be in spring 2020.
| Official | 19 February 2020 |
Analysis of overall satisfaction rates in the National Student Survey at sector level. Responses will be split by the following student characteristics: age, gender, ethnicity, disability status, subject, and mode of study. Benchmarks for each of these characteristics will be calculated using the remaining five.
| Official | 19 February 2020 |
Data on the number of full-time equivalent higher education students at providers which are registered with the Office for Students.
| Official | 12 February 2020 |
The Higher Education Students Early Statistics (HESES) survey gives an early indication of the number of higher education students studying in each academic year. It is used primarily to inform the allocation of teaching funding for the following academic year. We have published data for the 2019-20 academic year for providers in the Approved (fee cap) category of the OfS Register.
| Official | 11 February 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
Confirmed medical and dental student intake numbers from the medical and dental students survey.
| Official | 22 January 2020 |
Publication | Type of statistic | Publication date |
---|---|---|
An update of the data analysis of the association between unconditional offers and progression to and continuation in higher education. This update includes progression for English 18-year-olds who entered higher education in 2018-19 and continuation for those who entered higher education in 2016-17.
| Official | 30 October 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
A set of experimental measures that can be used to identify students groups that have different rates of continuation within, and access to, higher education.
| Experimental | 26 September 2019 |
The Higher Education Students Early Statistics (HESES) and Higher Education in Further Education Student (HEIFES) surveys give an early indication of the number of higher education students studying in each academic year. They are used primarily to inform the allocation of teaching funding for the following academic year. This is the third scheduled revision to the data that was originally published on 5 February 2019. It will include data for providers that have both registered with the Office for Students in the Approved (fee cap) category and signed off their HESES18 or HEIFES18 data by 20 September 2019.
| Official | 26 September 2019 |
Update of postcode to POLAR4 quintile data based on the May 2019 ONS postcode products. The previous release in September 2018 was based on the November 2017 ONS postcode products. Note: this data was updated on 9 January 2020 to include postcodes in Northern Ireland.
| Official | 25 September 2019 |
Experimental statistics produced using a newly developed measure of young participation in higher education by state-funded mainstream school pupils in England.
| Experimental | 25 September 2019 |
Updated experimental statistics on salaries three years after graduation added to the Unistats website, or its replacement. The Unistats dataset was released by HESA, as our designated data body. See | Experimental | 11 September 2019 |
Official data for undergraduate courses on each university and college’s National Student Survey scores, jobs and salaries after study and other key information for prospective students. The Unistats dataset was released by HESA, as our designated data body. See | Official | 11 September 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
The metrics and contextual data produced to inform Year Four of the TEF, published for all English providers potentially in scope for TEF Year Four assessment who have metrics data available. This scheduled revision to data that was originally published on 10 January 2019 will include metrics data for providers that have registered with the Office for Students between 24 January 2019 and 19 August 2019 and were not previously included in the data.
| Official | 29 August 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables. This scheduled revision to data that was originally published on 29 March 2019 will include data for providers that have registered with the Office for Students between 22 June 2019 and 19 July 2019.
| Official | 26 July 2019 |
An update to the analysis presented in the report (published December 2018), which will include individuals who graduated in the academic year 2017-18.
| Official | 11 July 2019 |
Statistics on students’ views about their courses from the 2019 National Student Survey. This data will be updated on a quarterly basis.
The Office for Students' key performance measure related to the National Student Survey ( ) was updated on 18 July 2019. | Official | 3 July 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables. This scheduled revision to data that was originally published on 29 March 2019 will include data for providers that have registered with the Office for Students between 27 May 2019 and 21 June 2019.
| Official | 28 June 2019 |
The Higher Education Students Early Statistics (HESES) and Higher Education in Further Education Student (HEIFES) surveys give an early indication of the number of higher education students studying in each academic year. They are used primarily to inform the allocation of teaching funding for the following academic year. This is the second scheduled revision to the data that was originally published on 5 February 2019. It will include data for providers that have both registered with the Office for Students in the Approved (fee cap) category and signed off their HESES18 or HEIFES18 data by 3 June 2019.
| Official | 13 June 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables. This scheduled revision to data that was originally published on 29 March 2019 will include data for providers that have registered with the Office for Students between 23 April 2019 and 27 May 2019.
| Official | 31 May 2019 |
Data for entrants, enrolled students and qualifying students, by equality and diversity characteristics.
| Official | 30 May 2019 |
Data summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
| Official | 24 May 2019 |
An update to the following key performance measures (KPMs): | Official | 2 May 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset consists of a visual and interactive dashboard of data, supported by accompanying data tables. This scheduled revision to data that was originally published on 29 March 2019 will include data for providers that have registered with the Office for Students between 20 March 2019 and 22 April 2019.
| Official | 26 April 2019 |
The Higher Education Students Early Statistics (HESES) and Higher Education in Further Education Student (HEIFES) surveys give an early indication of the number of higher education students studying in each academic year. They are used primarily to inform the allocation of teaching funding for the following academic year. This scheduled revision to the data that was originally published on 5 February 2019 will include data for providers that have both registered with the Office for Students in the Approved (fee cap) category and signed off their HESES18 or HEIFES18 data by 15 April 2019.
| Official | 25 April 2019 |
Data on the number of full-time equivalent higher education students at providers which are registered with the Office for Students.
| Official | 18 April 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
Data indicators that provide a sector-level picture of the patterns in access and participation across the student lifecycle, and also at individual provider level. Published for all English providers delivering undergraduate provision, the dataset will consist of a visual and interactive dashboard of data, supported by additional and more granular data tables.
| Official | 29 March 2019 Our quality assurance processes identified some outstanding issues with disclosure control and significance testing ahead of publication. Publication was delayed from 21 March to ensure that these matters are resolved, and that the outputs meet user requirements for quality and accessibility. |
An update to the following key performance measures (KPMs): | Official | 21 March 2019 |
Publication | Type of statistic | Publication date |
---|---|---|
Data showing the remuneration of senior staff at OfS funded providers (excluding further education and sixth-form colleges), and compensation for loss of office paid across the provider in 2017-18 and prior year 2016-17.
| Official | 12 February 2019 |
Early indication of the number of higher education students studying in 2018-19, used primarily to inform the allocation of teaching funding for 2019-20, from the Higher Education Students Early Statistics (HESES18) and Higher Education in Further Education Student (HEIFES18) surveys.
| Official | 5 February 2019 |
Publication | Type of statistic | Publication date |
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Transparent Approach to Costing (TRAC) data analysed by TRAC peer group.
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Data summarising sector aggregate income and costs by activity based on data from higher education providers required to implement and report under the Transparent Approach to Costing (TRAC) methodology.
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The United Kingdom is home to some of the world's oldest and most respected universities, but there's more to PhD study than age and reputation. Thankfully, British research programmes are also innovative and world-leading, with a modern approach to doctoral training and generous funding available to students from all backgrounds.
This guide covers everything you need to know about studying a PhD in the UK in 2024. We've explained how British PhD programmes work, what they cost (and how to pay for them) as well as advice on How to apply for a PhD in the UK.
We're celebrating international students in the UK by supporting the #WeAreInternational campaign .
UK universities carry out research in all major subject areas but recent years have seen increasing investment in priority areas such as AI, Machine Learning and related fields such as Health Science and Bioinformatics.
Alongside this, the UK Government is working to attract and support talented international researchers with the launch of a three-year post-study work visa and the extension of PhD studentships to overseas students .
Here are a few reasons to consider a PhD in the UK this year:
The UK is a member of the European Higher Education Area (EHEA). This means its qualifications follow the format of the Bologna process. A doctorate in the UK is a third-cycle qualification. It usually takes between three and four years to complete a UK PhD.
This guide focuses on how PhD programmes in the UK are structured including assessments, examinations and supervision.
The British PhD follows a format that's recognisable around the world (partly because many parts of the world have copied the British PhD format!).
At its core, a UK doctorate is an independent research qualification. Right from the beginning, the focus is on your own individual research project with the ultimate aim of producing an original thesis that contributes to the understanding of your field.
Unlike in some countries (such as the USA ), there isn't any formal taught components for a UK PhD. You probably will have some additional training and development opportunities during your doctorate (such as teaching undergraduates , attending conferences and publishing papers ) but your performance in these won't affect your final degree result.They will help you hone your skills and knowledge to increase your employability prospects though!
Most UK universities award their academic doctorates as PhD qualifications. However, some institutions award a DPhil instead. The two degrees are effectively the same; in fact, they even stand for the same thing ('PhD' is an abbreviation of the Latin philosophiae doctor , whereas 'DPhil' is an abbreviation of the English 'doctor of philosophy'). Rest assured that, for all intents and purposes, a DPhil is the same as a PhD.
Other UK doctoral degrees do differ. Some universities award specialised professional doctorates in particular subjects. Examples include the Doctor of Engineering (Eng.D), Doctor of Education (EdD) or the Doctor of Business Administration (DBA) . These tend to include more practice-based research and reflection, being designed for experienced professionals.
The length of a UK PhD (or other doctorate) is fairly standard. You'll normally be expected to spend a minimum of three years researching towards your thesis, with most universities allowing students to extend for a fourth year if necessary. Around six to eight years are normally allowed for part-time PhDs.
The UK academic year runs from September to June , but the lack of formal teaching on British doctoral programmes means that PhD students can, in principle, start at any point in the calendar year. Be aware that your university may prefer a September start where possible, however, in order to line up with induction and orientation.
At the start of your degree, you'll be partnered with at least one PhD supervisor . They will be an expert in your specialism with some relevant experience of the kinds of material you intend to research and the methods you expect to use. It's their job to guide your project and provide advice on the best direction for your research as you progress. Your supervisor will also support your professional development as a researcher and – potentially – as a future academic.
It's actually common for students in the UK to have two supervisors :
Sometimes the split in supervisor roles and responsibilities isn't as clear as this, with some students being co-supervised by two academics who both offer academic advice and more general support.
The UK PhD is traditionally a pure research degree, with no taught classes and assessments (other than your final oral examination – see below). You will normally begin with a literature review of existing work in your field, before moving on to gathering your own quantitative or qualitative data, textual evidence or other materials and eventually writing up your findings as a PhD thesis .
Some UK PhD students begin by registering for an MPhil before completing a PhD upgrade at the end of their first year (this is a short oral exam, based around a chapter draft or similar).
Some UK universities also offer a more structured PhD with timetabled training and development activities. This is most common for PhDs funded by the UK Research Councils which take place within dedicated Doctoral Training Centres.
At the end of your PhD you will submit a written thesis detailing your findings and the conclusions you have drawn from them. The length of a UK PhD thesis varies by subject. Dissertations in the Arts, Humanities and Social Sciences tend to be between 60,000 and 100,000 words. Dissertations in STEM subjects are shorter, as much of the information is conveyed through graphs and data tables.
At least one of your supervisors will read your PhD in full before you submit it and offer constructive feedback to help improve your thesis.
Your PhD will then be submitted for oral examination in a process known as a viva voce (Latin for 'living voice'). A UK PhD viva usually involves two examiners: one 'internal examiner' from within your university and one 'external examiner' from another institution. Both will read your thesis in advance and then question you about it. It is your job to 'defend' your findings and conclusions in order to prove the value of your research and confirm the PhD is your own work.
Unlike in other European countries , where the viva is often a public defence, UK PhDs are usually examined in a 'closed room' setting. Your supervisor is not usually present but should be available before and after the exam.
Immediately following your viva your examiners will recommend a PhD result for you. This may involve passing (with or without some corrections to your thesis) or other outcomes that may require additional research and / or resubmission (it's rare to completely fail your PhD after reaching the viva stage).
If you’re interested in studying in the UK then we’ve covered everything you need to know including what they cost (and how to pay for them as an international student ) as well as advice on how to apply for a PhD in the UK .
Search our database of PhD programmes in the UK .
The seven UK Research Councils provide government studentships for PhD research in different subject areas. Our simple guide explains how this funding works, what you can get and how to apply successfully.
You may be able to get a PhD loan of up to £27,892 for a UK doctorate from Student Finance. Our guide explains eligibility, applications and repayments.
Our guide explains the best ways to fund international PhD study in the UK, with information on all the main scholarships available to you.
Centres for Doctoral Training (CDTs) or Doctoral Training Centres (DTCs) provide UK Research Council funded PhD studentships to postgraduate students
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The drop out rates for America and Germany in University are similar at about 30%. I believe a common explanation for both is that there is not enough screening for the people who go into college.
However, googling tells me that in the UK it is about 5% ish. Why is their dropout rate so low?
The comparison between the dropout rates for British and German universities is a topic I have talked about with many collegagues familiar with both systems. The core reason seems to be that what is deemed an acceptable dropout rate in the UK is much, much lower than in Germany.
This then leads to all kinds of different actions and policies that lead to the actual dropout rates being so much lower.
British universites that can afford to be picky are usually very selective, and will only admit students they are confident will do well. On the other hand, for many subjects (eg math, CS), German universities will admit almost anyone with Abitur, and then just sort them out in the first year.
British universities tend to offer quite a bit of support for (weaker) students. How much and the nature varies - most universities couldn't afford the immense attention paid to individual students at Oxbridge, but there is bound to be some effort. I've never heard of comparable attempts at German universities.
Lest I give the impression that I consider the British approach superior, UK universities that cannot afford to be that picky in admissions will just lower the standards to the point where almost everyone they do admit will pass (with whatever support they can offer). German professors tend to have much more latitude to only let those pass they deem worthy.
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A new report from Debut Careers reveals the UK universities with the highest and lowest student dropout rates.
6 in every 100 (6.3%) - that's how many students drop out of university in the UK on average. With drop-out rates amongst UK students rising, perhaps there is a need to look closer into the divide - and that’s what the team at Debut Careers have done.
Looking into dropout rates of over 140 of the country's higher education institutions, they can reveal the universities with the highest and lowest percentage of students who have dropped out of their courses.
London Metropolitan University has the highest dropout rates in the UK at a huge 18.6%, while the University of Cambridge has the lowest. The more ‘prestigious’ and well-known institutions seem to sit at the top of the league, with a certain degree of stability evident.
The research, from student careers service, Debut Careers , also shows the dropout rates by subject. Their insights reveal that computer sciences are the subjects which suffer from the highest dropout rate at 9.8%, with medicine, dentistry and veterinary science being the subjects which see the lowest, at just 1.5%.
There could be many reasons for a student to drop out of education. No matter your background or university choice, the sudden change in lifestyle can sometimes be too much to cope with. For many, it can be an overwhelming shift.
Whatever the reason you may have for considering dropping out, it's important to look into your options. If you are beginning to feel withdrawn or see grades slipping, take a moment to address these and question why, considering what your next steps could be. It could be that instead of dropping out altogether, you could transfer to a new course or uni .
Keep in mind that support is out there and talking to someone is always a great help. After all, most students who need support don't tend to ask for help.
Usmann Qureshi, Client Success Manager from Debut Careers, believes that while leaving an undergraduate course does limit some opportunities, it opens many doors to other exciting ones:
“Leaving an undergraduate course before completion does rule out some opportunities, but opens up many others.
“When we speak with candidates, they are often really surprised at the companies with opportunities that are still available to them for Higher/Degree apprenticeships.
“The apprenticeship levy has changed the landscape. Not only is there a much bigger incentive for employers to bring on apprentices, but the data suggests they are often much better value. Retention rates are roughly double that of graduates and often the employee can contribute positively much earlier on.”
The research also looked into the difference in dropout rate by subject between 2007/08 and 2017/18. From this, we can see that every subject has seen a reduced dropout rate over time. Combined subjects have reduced their dropout rate by 23.8%, while physical sciences have dropped by 12.7%. Medicine, dentistry and veterinary science saw the smallest drop of 1.4%, followed by education, which had a reduction of 3.4%.
Does this show that the decision of attending university is becoming a much more thought-about choice? Are institutions doing more to keep their students happy or is it that the alternative options of internships and schemes are being taken advantage of instead? If there is such a need to improve student retention, maybe measures such as more student advisors were put into place in recent years?
Regardless of the reasons for dropout rates, the most important solution to this problem is to address any worries in a timely manner and know that there are numerous options today. There are so many support systems, and it's crucial that you make use of them; from recruitment companies to internships, schemes, work experience and much more, there are so many avenues you may have not explored if your first choice was university study.
Often, you won’t know that university wasn't right for you until you get there and stand in the lecture hall. Sometimes it just won't work out and the reality isn't quite what you expected. And guess what? That's more than ok. It's not an easy position to be in, but don't worry, you're not the only one.
- Best university cities for different types of students
- How to reach out for mental health support at uni
You can see the full research and more advice and guidance into future career and education options from Debut Careers in their career insights here .
There are loads of excellent courses available for you to study at unis across the UK
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6.2 per cent of students at Queen’s University Belfast don’t finish their degrees
Surely at some point every student has sobbed from the stress of studying their subject, collapsed onto the carpet with a load of crisps, and contemplated putting an end to their suffering by dropping out. But how many students at Russell Group unis actually had the guts to admit defeat and become a dropout in 2024?
As part of The Guardian’s “The best UK universities 2025 – rankings” this year, The Guardian gathered data on what percentage of freshers make it through to second year. So, this info doesn’t factor in how many students defer the rest of their degrees to another year – a thing which some unis encourage struggling students to do instead of dropping out.
Dropout rates at Russell Group unis are generally quite low. There are 87 UK unis on The Guardian’s league table which have higher drop out rates than any Russell Group uni.
Oxford and Cambridge are the Russell Group unis with the lowest dropout rates in 2024. According to the data The Guardian had, just 1 per cent of Cambridge students give up on their degrees. Perhaps it’s so much effort to get into Oxbridge that people are too scared to drop out if they hate it.
Queen’s University Belfast has the highest drop out rate of any Russell Group uni. A massive 6.2 per cent of students didn’t finish their degrees. Who knows what they do to students over there?
So, here is every Russell Group uni ranked by the percentage of freshers who don’t make it to second year, from highest to lowest:
24. University of Cambridge – 1.0 per cent drop out
23. University of Oxford – 2.0 per cent drop out
I suppose if you’ve gone through the effort of memorising a whole textbook for your Oxford interview, you’re less likely to leave (Credit: Francesca Gunner)
22. Durham University – 2.5 per cent drop out
21. University of Edinburgh – 2.9 per cent drop out
=19. University of Bristol – 3.4 per cent drop out
=19. London School of Economics and Political Science (LSE) – 3.4 per cent drop out
18. University College London (UCL) – 3.6 per cent drop out
17. University of Warwick – 3.7 per cent drop out
=15. University of Manchester – 3.8 per cent drop out
=15. University of Exeter – 3.8 per cent drop out
Maybe some freshers see how uninspiring the buildings at the main Exeter campus look and decide to go home? (Credit: Francesca Gunner)
=12. University of Leeds – 4.0 per cent drop out
=12. University of Birmingham – 4.0 per cent drop out
=12. University of York – 4.0 per cent drop out
11. University of Liverpool – 4.1 per cent drop out
=9. University of Nottingham – 4.3 per cent drop out
=9. University of Sheffield – 4.3 per cent drop out
8. Imperial College London – 4.4 per cent drop out
=6. Queen Mary University of London – 4.7 per cent drop out
=6. Newcastle University – 4.7 per cent drop out
5. University of Southampton – 4.9 per cent drop out
4. University of Glasgow – 5.3 per cent drop out
=2. King’s College London – 5.5 per cent drop out
=2. Cardiff University – 5.5 per cent drop out
1. Queen’s University Belfast – 6.2 per cent drop out
Brb just regretting all my life choices
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Three singles in – time to get serious
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‘That b****h should own a Sunglass Hut because she’s so shady’
We will not love Dark Horse for life, lifetimes
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They were on a reality TV show together
Of course her character name is ‘pretty nurse’
These are so cute I can’t cope
Heather is booked and busy
They are not happy
From turning down the White House to cancelling concerts, there’s a lot
I honestly wish I could pretend to get fired just to make a customer feel bad
Moo Deng the pygmy hippo that you are
Grab a calculator and figure out your UCAS points
Homophobia and divorce isn’t ‘drama’ – it’s heartbreak
This man is such a menace
He also says he actually likes the ending!? I thought Tyrion was meant to be smart
*Pretends to be shocked*
Is anything on this show real?
LEXINGTON, Ky. (Sept. 13, 2024) — It’s a simple, but powerful formula: more students succeeding at higher rates means a more skilled, healthy and educated workforce to meet Kentucky’s needs.
By those measures, University of Kentucky President Eli Capilouto told members of the Board of Trustees on Friday, UK is making “historic strides in its mission to advance the state in terms of the numbers of students we are educating, retaining and graduating.”
“We know that what our students do here — and how successful they are at UK — will determine, in large measure, whether we are successful in advancing Kentucky,” Capilouto said. “Our mission — now more than at any time in our history — is to open our doors as wide as possible to more and more students, who will shape the future of this state. These numbers reflect our commitment to that ideal. These numbers reflect that we are Kentucky.”
Here are the details:
Finally, the university’s efforts to utilize innovative programs to propel student success also are paying off.
UK Invests — a first-of-its-kind program that debuted last year — provides access to low-risk investment accounts to every student, with opportunities for students to earn money for developing healthy habits. In the first year of the program, more than 20% of students have opened an account.
The retention rate of those students who were in their first year was about 92%, compared to a little more than 84% for those who did not participate. First-year students with UK Invests accounts also had higher GPAs — 3.3 compared to 3.0 who did not participate. More importantly, these gains are seen across levels of incoming student preparation.
Already this fall, more than one-third of new first-year students have opened UK Invests accounts.
“We exist to advance this state in everything that we do. And we are united in our focus on that mission,” Capilouto said. “The enrollment numbers represent one chapter in the story we are writing about what we are doing to honor that vision and achieve our mission. We don’t intend to stop. There are still too many chapters to write, too many stories to tell, so much more to do for our students, their families and this state whose name we bear.”
As the state’s flagship, land-grant institution, the University of Kentucky exists to advance the Commonwealth. We do that by preparing the next generation of leaders — placing students at the heart of everything we do — and transforming the lives of Kentuckians through education, research and creative work, service and health care. We pride ourselves on being a catalyst for breakthroughs and a force for healing, a place where ingenuity unfolds. It's all made possible by our people — visionaries, disruptors and pioneers — who make up 200 academic programs, a $476.5 million research and development enterprise and a world-class medical center, all on one campus.
In 2022, UK was ranked by Forbes as one of the “Best Employers for New Grads” and named a “Diversity Champion” by INSIGHT into Diversity, a testament to our commitment to advance Kentucky and create a community of belonging for everyone. While our mission looks different in many ways than it did in 1865, the vision of service to our Commonwealth and the world remains the same. We are the University for Kentucky.
Uk extension hosts morocco group to learn new ideas for improving communities back home, uk offers educational events, programming for latinx and hispanic heritage month, kgs awarded imls grant to preserve nearly 200 years of kentucky geological data, a legacy of service: becknell scholarship supports future health care leaders.
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The new Graduate Route opened for applications on 1 July 2021 to international students who successfully complete a degree at undergraduate level or above in the UK. It allows students on the Graduate Route to work or look for work after their studies for a maximum period of 2 years or 3 years for doctoral students. More information can be found on our International Student Support webpages .
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To summarise, based on the analysis of 26,076 PhD candidates at 14 universities between 2006 and 2017, the PhD pass rate in the UK is 80.5%. Of the 19.5% of students who fail, 3.3% is attributed to students failing their viva and the remaining 16.2% is attributed to students leaving their programme early. The above statistics indicate that ...
May 5, 2021. By Luke Payne. One in four Durham PhD students leave study without achieving their target doctorate degree. The data, acquired through a Palatinate Freedom of Information request, reveals performance is particularly poor in certain departments, with Computer Science, Education, and History performing amongst the worst in their ...
Student dropout rates in the UK. On average, 6 in 100 (6.3%) students in the UK drop out of university. Since 2007/08, dropout rates have decreased by 1.1%, from 7.3% in 2008, to 6.2% a decade later. [6] This would mean an average of 48,548 students each year drop out. Universities with the highest dropout rates in the UK
61% were undergraduate qualifications and 39% were postgraduate. 80% of graduates were in employment or unpaid work 15 months after graduation. 19% of graduates were in further study. Median salaries for graduates were £9,500 higher than non-graduates. 5.3% of non-graduates were unemployed compared to 3.7% of graduates.
Turning to consider employment outcomes by sector, figure 1 demonstrates that the vast majority of UK PhD holders (70.1 per cent) have left the academic sector three-and-a-half years after graduation. Of those who remain in academia, one-third are undertaking research, while two-thirds occupy the role 'higher education teaching professional'.
Doctoral attrition is high in many countries, with reported rates of up to 40 to 50% of postgraduate researchers (PGRs) terminating their PhD studies before completion (Geven et al., 2017; Litalien & Guay, 2015).Attrition can be considered a process, in which PGRs weigh the costs and benefits of persisting or discontinuing, and then do or do not actually end their studies accordingly (Jaksztat ...
Abstract. Postgraduate education has become increasingly crucial for nations in recent years, contributing to scientific, technological, and social progress. However, high dropout rates may undermine the benefits of postgraduate education. This study aims to identify which individual, academic, socio-economic, and institutional variables ...
The number of PhD students winning UK research council funding has fallen sharply in recent years, according to data that shows that domestic candidates are increasingly being squeezed out by international applicants. Figures obtained by Times Higher Education show that UK Research and Innovation funding councils supported 5,580 doctoral ...
Before the pandemic, one in five research students were expected to disengage from their PhD. Disengagement includes taking extended leave, suspending their studies or dropping out entirely. COVID ...
Details. These experimental statistics use graduate outcome data to look at employment and earnings of higher education graduates 1, 3, 5 and 10 years after graduation. They use the same ...
As students prepare for the new term, the Higher Education Policy Institute is publishing a report on non-continuation (or 'drop-out') rates. A short guide to non-continuation in UK universities (HEPI Policy Note 28) by Nick Hillman, HEPI's Director, looks at the scale of the problem, including showing that the UK has the lowest drop-out rate of […]
Some quantitative information on dropout rates can be found for UK institutions and US. I think the US data is the same source the OP linked. ... As a freshly minted PhD, I would suggest that the drop-out rate must be combined with more qualitative assessments, such as mentor conflicts, level of committee involvement, administrative ...
It's a different story in the US, where, according to the Survey of Earned Doctorates, students take an average of 5.7 years to graduate. 4. UK PhD fees tend to be lower. Fees err on the more ...
In the research literature about students dropping out of doctoral programs (or "attrition", as they call it), very often the ballpark of 40-60% attrition rate is mentioned 1. Imagine you are in a classroom with your peer Ph.D. students. Look to the person on your left. Look to the person on your right.
HE Student Data: Frequently asked questions. We publish a wide range of tables and charts about students in higher education. The areas above guide you through the information we collect, and we have also published a complete list of our tables.. The table below identifies the charts and tables that answer some of the most common questions about HE students.
Annual release of UK-wide confirmed intakes for 2022-23 and the initial intakes for 2023-24. ... The data combines projected completion rates with graduate outcomes data to create projected rates of progression from entry to professional employment or further study. ... Updated experimental statistics on salaries three years after graduation ...
Our simple guide explains how this funding works, what you can get and how to apply successfully. Read more. PhD Loans for Doctoral Students - A Guide for 2024. United Kingdom Student Loans Funding. You may be able to get a PhD loan of up to £27,892 for a UK doctorate.
20. The comparison between the dropout rates for British and German universities is a topic I have talked about with many collegagues familiar with both systems. The core reason seems to be that what is deemed an acceptable dropout rate in the UK is much, much lower than in Germany. This then leads to all kinds of different actions and policies ...
University dropout rates reach new high, figures suggest. ... By comparison, the number of students enrolling on degrees in the UK rose by almost 11% between 2018-19 and 2021-22.
Depending on where students study in the UK, they will graduate with varying levels of student loan debt. Students graduating in England in 2023 will have accrued on average 44,940 British pounds ...
Middlesex University (12.6% dropout rate) Plymouth College of Art (12.5% dropout rate) University of Wolverhampton (12.2% dropout rate) University of Abertay Dundee (12.1% dropout rate) London Metropolitan University has the highest dropout rates in the UK at a huge 18.6%, while the University of Cambridge has the lowest. The more ...
The graduation rate is more than 71%, which tracks the number of students who enroll at UK and graduate within six years. Nationally, the six-year graduation rate is 62.2%, according to the ...
There are 87 UK unis on The Guardian's league table which have higher drop out rates than any Russell Group uni. Oxford and Cambridge are the Russell Group unis with the lowest dropout rates in ...
UK's six-year graduation rate is now more than 71% — another record that places the university among the top 100 public, four-year institutions in America, based on the most recent data. UK's four-year graduation rate is now a little more than 60% — almost 30 percentage points higher than it was 15 years ago. Preliminary retention ...
Graduate Route - a graduate visa gives you permission to stay in the UK for at least 2 years after graduation. Skip to main content The University of Glasgow uses cookies for analytics.