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