The application process and getting to grips with Athena SWAN statistics Sean McWhinnie Oxford Research and Policy Both men and women benefit from good practice, however, women in particular are adversely affected by bad practice Outline •Background •Requirements •Examples, thoughts, and hints What are Athena SWAN awards? • Key assessment areas: Knowing the baseline and SET academic profile Providing positive support for women at key career transition points Changing the culture and gender balance in decision making Work-life balance practices, their introduction and uptake Champions, responsibilities and accountabilities A Silver department award • Silver department Significant record of activity and achievement Identified particular challenges Implemented activities Can demonstrate the impact of these activities Silver department award application • Letter of endorsement from Head of Department • Self-assessment process • Picture of the department • Supporting and advancing women’s careers • Any other comments • Action plan • Case studies Students Numbers: what is parity? 70 Overall proportion of female students Undergraduate 60 56 56 56 59 58 57 56 Postgraduate 53 52 46 47 48 49 49 2003-04 2004-05 59 59 54 54 2007-08 2008-09 58 53 53 2009-10 2010-11 % Female 50 59 40 30 20 2000-01 2001-02 2002-03 2005-06 2006-07 Year of graduation Requirements Gather your initial qualitative and quantitative evidence together, to form the basis of your action plan. Requirements • A robust evidence base • What you are doing to create a pipeline for future appointments in your discipline? • The SWAN panel members are interested in recruitment, retention and promotion • You should provide reflective narrative on what the data indicate Key gender-disaggregated quantitative evidence Student data • Numbers • Undergraduate applications, offers and admissions • Class of degree awarded • Postgraduate applications, admissions and completions (taught and research) • At least 3 years’ data Year Total UG headcount First-year UG entrants Male %F Female 2008-09 175 115 39.7% 2009-10 219 129 37.1% 2010-11 237 143 37.6% 2011-12 267 148 35.7% 2008-09 66 45 40.7% 2009-10 81 43 34.7% 2010-11 68 43 38.7% 2011-12 97 56 36.6% 300 Total Undergraduates 37.6% F 250 200 35.7% F 37.1% F 39.7% F Male 150 Female 100 50 0 2008-09 2009-10 2010-11 2011-12 Year Total UG headcount First-year UG entrants Male Female %F 2008-09 175 115 39.7% 2009-10 219 129 37.1% 2010-11 237 143 37.6% 2011-12 267 148 35.7% 2008-09 66 45 40.7% 2009-10 81 43 34.7% 2010-11 68 43 38.7% 2011-12 97 56 36.6% 300 Total Undergraduates 37.6% F 250 200 35.7% F 37.1% F 39.7% F Male 150 Female 100 50 0 2008-09 2009-10 2010-11 2011-12 Postgraduate research students • If numbers are small (e.g. foundation and masters courses) then consider using rolling averages • You need to break you data down into (sensible) subjects • You need benchmarking data; but think carefully – use subject data not subject groups Benchmarking University National C1 Bi ol ogy 63.8% 56.4% C3 Zool ogy 63.6% 58.4% C7 Mol e cul a r bi ol ogy a nd bi oche mi s try 55.0% 52.6% JACS code and subject Don’t fall into the trap of stating that you’re doing better than the national average and saying that therefore everything is fine – there’s probably still a leaky pipeline in respect of women’s progression Engineering programmes – Computer Systems Engineering (MEng/BEng) – Electronic and Communications Engineering (MEng/BEng) – Electronic and Computer Systems (BEng) Digital Media programmes – Digital Arts (BA) – Drama and Multimedia (BA) – Multimedia Technology and Design (BSc) Female applicat ion-t o-ent ry progress for undergraduat e programmes Applications Offers Acceptances Entries Applications-to-entries conversion rate % Offers-to-entries conversion rate % M 659 440 133 56 2009-10 F 859 610 202 103 %F 56.6 58.1 60.3 64.8 M 786 514 180 92 2010-11 F 1009 711 236 133 %F 56.2 58.0 56.7 59.1 M 978 469 191 92 2011-12 F 1251 681 271 125 8.5% 12.0% 11.7% 13.2% 9.4% 10.0% 12.7% 16.9% 17.9% 18.7% 19.6% 18.4% %F 56.1 59.2 58.7 57.6 Percentage of postgraduate students who are female 40 Applications 35 Offers Admissions 30 25 % 20 15 10 5 0 2006/07 2007/08 2008/09 2009/10 Example: Degree classification of graduates in physics 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Women Men Women 2004/05 Men 2005/06 Women Men Women 2006/07 First class honours Upper second class honours Third class honours / Pass Unclassified Men 2007/08 Women Men 2008/09 Lower second class honours Example: Degree classification of graduates in physics Remember to give an indication of the numbers of students 2009 2010 2011 First 2i 2ii Third Totals M 9 15 5 0 29 %M 52.9 50 55.6 - 51.8 F 8 15 4 0 27 %F 47.1 50 44.4 - 48.2 M 3 15 12 4 34 %M 25 71.4 80 66.7 63 F 9 6 3 2 20 %F 75 28.6 20 33.3 37 M 9 17 13 4 43 %M 39.1 51.5 76.5 80 55.1 F 14 16 4 1 35 %F 60.9 48.5 23.5 20 44.9 50% 40% Proportion of students awarded each degree class 2011 46% 40% 40% 30% 30% M F 21% 20% 11% 10% 9% 3% 0% First 2i 2ii Third 50% 40% Proportion of students awarded each degree class 20092011 38% 45% 44% 28% 30% 20% M F 20% 13% 8% 10% 4% 0% First 2i 2ii Third The SWAN panel members are looking for evidence that you know where you stand and that you are using the data to inform your action planning Staff data • Applications, shortlists and appointments for research and academic posts (internal and external applications) • Applications and appointments for promotions for research and academic posts • Completion of appraisals • Take up of flexible and part-time working options, caring leave and career breaks • Seminar or colloquia speakers Staff: Biological Sciences Researcher Lecturer 2009 2010 2011 M F %F M F %F M F %F Senior Lecturer/ Reader Pro fesso r To t al 16 11 10 9 46 19 6 2 2 29 54.3 35.3 16.7 18.2 38.7 19 13 10 8 50 17 6 2 2 26 47.2 27.8 16.7 20 34.2 20 12 9 8 49 19 6 2 2 29 48.7 33.3 18.2 20 37.2 Female:Male ratio of academic staff in Natural Sciences Dept 120% 100% 80% 2006/7 60% 2007/8 2008/9 40% 20% Lecturer Senior lecturer Reader Professorial Male Female Male Female Male Female Male Female Male Female 0% Researcher 30 25 31 8 14 10 69 88 124 367 370 372 80% 65 90% 49 100% 60 Female:Male ratio of academic staff per grade in STEM 70% Female Male 206 227 54 64 68 163 222 130 157 190 587 575 30% 559 40% 224 50% 240 60% 20% 10% 0% 2006 2007 2008 2006 2007 2008 2006 2007 2008 2006 2007 2008 2006 2007 2008 Researcher Lecturer Senior Lecturer Reader Professor Benchmarking staff data • HESA staff data are grouped into relatively broad cost centres • Recently HESA stopped collecting data broken down into staff grades, except professor • Plot the full pipeline using your student and staff data. This will show you where women are leaving the (academic) pipeline. • Where are the women going? Are they going to different places compared to the men? Pipeline: progression in maths by gender, 2007/08 45 40 39.2 Proportion Female (%) 35 29.5 30 25 26.8 22.4 20.4 20 15 10 4.5 5 0 Undergraduates Postgraduates Researchers Lecturers Senior Lecturers/ Readers Professors Pipeline: progression in maths by gender, 2007/08 96.5 100 77.6 80 60 70.5 59.4 73.2 60.8 49.7 % 40 79.6 50.3 40.6 Women 39.2 Men 29.5 22.4 26.8 20.4 20 4.5 0 GCSE A Level Undergraduate Postgraduate Researcher Lecturer Senior Lecturer Professor Data source: HESA (2008) Pipeline: progression in chemistry by gender, 2007/08 100 94 86 80 60 70 54.8 51.6 45.2 % 56.3 43.7 48.4 40 74 61.5 Women 38.5 30 Men 26 20 14 6 0 GCSE A Level Undergrad Postgrad Researchers Lecturers Senior Lecturers Professor Data source: HESA (2008) Pipeline: Progression in physics by gender, 2007/08 100 88.8 79.8 80 60 78.4 82.7 74.3 94.6 80.2 55.8 44.2 % Women Men 40 22.2 20 21.6 25.7 17.3 19.8 11.2 5.4 0 GCSE A Level Undergrad Postgrad Researcher Lecturer Senior Lecturer Professor Data source: HESA (2008) Pipeline: progression in biology by gender 2007/08 100 81.9 80 63.6 60 47.1 % 64.6 57.3 64.1 52.3 50.7 Women 42.7 40 47.7 52.9 36.4 49.3 35.9 35.4 Men 18.1 20 0 GCSE A Level Undergrad Postgrad Researcher Lecturer Senior Lecturer Professor Data source: HESA (2008) Turnover data • The key issue is the proportions of men and women at each grade who leave. Number of leavers by grade Researchers Lecturers Year F M F M 2011-12 5 4 0 0 2010-11 1 4 0 1 2009-10 5 8 0 0 Turnover data • The key issue is the proportions of men and women at each grade who leave. Proportion of leavers at each grade Researchers Year Lecturers F M F M 2011-12 35% 25% 0% 0% 2010-11 20% 27% 0% 20% 2009-10 40% 22% 0% 0% Turnover data • The key issue is the proportions of men and women at each grade who leave. • The bulk of your leavers are likely to be at researcher level: are the leaving rates for men and women similar? Applications Applied Shortlisted Interview ed Appointed Female 162 36 29 13 Male 460 73 52 21 26.0% 33.0% 35.8% 38.2% 32 6 5 3 135 17 15 5 19.2% 26.1% 25.0% 37.5% 26 1 1 1 48 8 4 1 35.1% 11.1% 20.0% 50.0% 8 0 0 0 56 9 7 2 12.5% 0.0% 0.0% 0.0% Pro gress Researcher %F Female Lect urer Male %F Female Senio r Male Lect urer/Reader %F Female Pro fesso r Male %F Key issues: context • How do the data compare to the national picture? • Are differences significant? • Are there any local factors? (Perhaps all subjects at your HEI have a lower proportion of female undergraduates than the national averages) Key issues: trends • How do the data vary with time? • Is the proportion of women increasing? • Are success rates for men and women the same? • Are the differences significant? • Beware of what’s hidden by percentages You can go further........ Contextualise yourself: Proportion of graduates from maths departments who were female 2008/09 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Proportion of academic staff in maths departments who are female 70% Percentage of female academic staff in maths departments with 10 or more academic staff 60% 50% 40% 30% 20% 10% 0% Data source: HESA (2008) Proportion permanent academic staff who are professors by age 80 67 Physics % Staff 60 45 40 20 31 8 39 4 0 31-40 41-50 Age of Staff Male 51-60 Female Data source: HESA (2010) Proportion permanent academic staff who are professors by age 80 Electrical Electronic & Computer Engineering % Staff 60 36 40 20 25 4 27 17 3 0 31-40 41-50 Age of Staff Male 51-60 Female Data source: HESA (2010) Proportion permanent academic staff who are professors by age 80 Biosciences % Staff 60 47 40 20 29 28 12 4 1 0 31-40 41-50 Age of Staff Male 51-60 Female Data source: HESA (2010) Overall gender balance on influential university committees Male Female 400 350 300 250 200 150 337 324 293 100 50 91 89 106 0 2006 2007 2008 • What’s the overall gender ratio of staff in the university? • Are women “well” represented in the university? • How does this compare for STEMM departments? It may not always be appropriate to plot all the data Nobody will be surprised to learn that the proportion of women in engineering is lower than that in the whole HEI • How does it compare to the national picture? • Is it increasing? • What are you doing to change it? When using the data for action planning • The key thing is that you know where you stand and that you use that understanding to inform your plans • What you observe is unlikely to be unique so others may have action plans that you can look at • Be realistic: stating that the proportion of female professors in computer science will be 50 percent in 5 years time may be a little ambitious! Summary • Present what you are asked for (if you can) • Make things easy for the panel and make sensible comparisons to contextualise the data • Be aware of the national picture • Use the data to inform your action plan Hints • Percentages/ratios are often best for comparisons though it is helpful to have an idea of the overall figures (significance) • Present data for men and women in same table • Trends over time rather than snapshots • Avoid too many pages of numbers • Don’t make the SWAN panel members work too hard to understand the data Sources of data • Sean McWhinnie, Oxford Research and Policy, can produce customised HESA data for benchmarking purposes • HESA (HEIDI) • The University • Equality Challenge Unit • Some learned societies • ASSET, CROS, PRES Thank you Sean McWhinnie Tel: 01235 439188 Email: sean.mcwhinnie@oxfordresearchandpolicy.co.uk