General questions:
Why study graduation rates?
Because they are what we have access to on a broad scale. Other metrics might be more powerful; for instance, comparing students with an initial interest in majoring in physics to who actually graduate. That data, however, is not measured nor reported in a consistent, accessible way.
My department has low enrollment, so how should I interpret the effectiveness score?
With extreme caution. For small programs, these numbers are influenced by decisions and life circumstances of individual students. Nonetheless, they can serve as a point for reflection for a department. Have there been women who showed initial interest in majoring in physics but did not persist? Or women you were able to support and encourage despite initial hesitation?
In our analysis, institutions with very low enrollment are excluded (enrollment of less than one woman per year, on average over 10 years).
What is the specific effectiveness score for each school? Why don’t you report this?
The effectiveness score is a measure of the percentage of women in each category—all women, women of color, underrepresented women of color—a disciplinary department graduates compared to the percentages of these populations the overall institution graduates. CWCS has calculated up to three effectiveness scores for each school, provided that their graduation data are available in IPEDS for the disciplines of focus for the project: computer science, mathematics, and physics.
How can I compare results between countries?
In each country, our primary interest is in providing access to data about the underrepresentation of women of color in STEM. We recognize that the demographics of underrepresentation will be different for each nation. In the US for example, our focus is on women overall, women of color (WoC) in particular, and women of color who are the most underserved in the disciplines of focus, in this case black women, Latinas, and Indigenous women
In the UK, we focus on women and Black, Asian, and Minority Ethnic women. Within the last five to ten years, UK universities and the UK government (including policy makers) have begun to extend options on censuses and official forms to include more than the female/male gender dichotomy. Whilst many universities have implemented policies and strategies to embrace all genders, the most recent UK census did not fully reflect other options beyond female/male. The 2021 Census may be the first to do so. The calculations for this project reflect the data that HESA collects, and as such, includes as female all those who ticked the ‘female’ category on official university forms, which universities then reported to HESA. Thus, when we specify ‘female’, we mean all those individuals who identify as female and have ticked that box on their official university forms.
For more information:
Gender identity–Office for National Statistics
Student 2019/20 – Gender identity | HESA
The UK government delineates 18 ethnic groups, with the option of choosing ‘other’ embedded within each of the five larger categories: White; Mixed or Multiple ethnic groups; Asian or Asian British; Black, African, Caribbean or Black British; and Other ethnic group. These groups are adjusted by country; for example, in Wales, ‘Welsh’ is the first option within the ‘White’ set of possible choices. The government tries to adapt to changes in ethnic identifications based on census answers.
For more information:
List of ethnic groups – GOV.UK
50 years of collecting ethnicity data – History of government
Ethnic group, national identity, religion and language
In each case, we are focused on women citizens (US citizens and permanent residents, and UK home students) as our interest is in those women who experience their main educational experiences in each nation, but who are underserved by the existing system.
For each country, the departments identified as most effective will be further investigated to discover best practices. It will then be possible to compare the results of the qualitative research to find practices and environments that are best for recruiting, mentoring, retaining, and graduating women and women of colour.
How can I get involved in this project?
Email us at cwcsresearch@gmail.com!
If you’d like to contribute to data: these numbers are the baseline for a larger project. We hope to study schools with better-than-expected graduation rates of women and women of color, to identify actions taken by professors at those institutions, department or institutional policies, or any other factors that might be adopted by other institutions. If you would like to volunteer as a case study site for our team, or carry out a self-study, we would love to hear from you! Please email us at cwcsresearch@gmail.com!
If you’d like to contribute to analysis or writing: We are delighted to work with researchers with expertise in qualitative analysis, quantitative analysis, and science communication. We are especially looking for research partners with expertise in countries outside of the US and the UK. If you’d like to partner with us, please email at cwcsresearch@gmail.com!
If you found a bug, oversight, or error in our site: Sorry! Please email at cwcsresearch@gmail.com so that we can attend to it.
My institution’s numbers are not all that great. What can I do to further investigate them? What can I do to improve them?
To further investigate how your institution is doing, you could conduct (or review existing) a departmental or institutional self-study or climate survey. Your institution probably has resources to help you conceptualize and perform your self-study. In the US, those resources are often concentrated in an office of institutional assessment or evaluation; in the UK, you will have institutional and faculty equality champions who are involved with Juno (https://www.iop.org/about/IOP-diversity-inclusion/project-juno#gref), Athena SWAN (https://www.advance-he.ac.uk/equality-charters/athena-swan-charter), Race Equality Charter (https://www.advance-he.ac.uk/equality-charters/race-equality-charter).
To help identify areas for growth and improvement, you can partner with another school (in your area or with similar demographics to you) whose numbers are different, to better understand their practices.
Long term, a goal of this project is to develop strong case studies and evidence-based recommendations for exemplary practices, moderated by institutional settings. We plan to develop a database of institutionally-grounded qualitative data augmented by quantitative rubrics.
Outside of your institution, there is a rich and complex landscape of resources and professional organizations for understanding departmental roles in supporting students of color.
US resources:
TEAM-UP Project | American Institute of Physics (with a focus on Black students in physics, including a rubric for departmental climate self-study)
AAAS SEA Change (with a focus on diversity in STEMM)
UK resources:
Project Juno | Institute of Physics (with a focus on women in physics)
Athena Swan Charter | Advance HE (with a focus on women science)
Race Equality Charter | Advance HE (with a focus on racial diversity in universities)
Note that these resources were developed in particular countries and disciplines, but may have insights for people in any discipline, in any country.
US-specific questions:
What is the effectiveness score?
A department’s inclusion effectiveness score is the ratio between the population’s representation among bachelor’s earners in the department and their representation among all bachelor’s earners at the institution. A larger score means the population is more represented in the department and a score of 1 means that the population is represented equally in the department and the institution.
We used the effectiveness score to categorize departments. Departments that had no bachelor’s earners in the considered population are categorized as “none.” Departments that did have bachelor’s earners in the considered population were categorized as “below average,” “average,” or “above average.” These categories were determined by splitting the scores into 3 quantiles.
What’s the difference between women of color (WoC), Black, indigenous and other people of color (BIPOC), historically excluded minority (HEM), and underrepresented minority (URM) categories? Which are you using, and why?
We are using these category labels for US data: Women, WoC, HEM women. We have chosen them somewhat arbitrarily, with an awareness that our choices have consequences.
By women of color (WoC), we mean women classified in the IPEDS data base as American Indian or Alaska Native; Asian; Black or African American; Hispanic or Latino (of any race); Native Hawaiian or other Pacific Islander; and two or more races.
By historically excluded minority (HEM) women, we mean women classified in the IPEDS data base as Black or African American; Hispanic or Latino (of any race); or American Indian or Alaska Native.
The classification of Black, indigenous and other people of color (BIPOC) women is analogous to the way we are using women of color in this project. The classification of underrepresented minority (URM) is analogous to historically excluded minority.
Where do these data come from? Which institutions are included, and why?
Data for the US effectiveness scores is calculated from the Integrated Postsecondary Education Data System (IPEDS), collected annually by the US Department of Education’s National Center for Education Statistics (NCES), and used by the National Science Foundation in their National Center for Science and Engineering Statistics. (Note that is the same source used by the National Science Foundation in their National Center for Science and Engineering Statistics)
We calculate effectiveness scores for all institutions that issue bachelors degrees in the disciplines of focus: computer science, mathematics and physics. We exclude any institution that technically offers a major in one of these areas, but has graduated fewer than one student per year, on average, over the past 10 years.
Why isn’t my school displayed on this graph?
Any institution that graduated an average of one student or fewer in the focus discipline between 2008 and 2020 has been excluded. In the US, institutions that don’t submit information to IPEDS are also excluded.
UK-specific questions:
What is the effectiveness score?
A department’s inclusion effectiveness score combines data on bachelors degree attainment, <OtherA>, <OtherB>, and <OtherC>, to display how well the department aligns with overall institutional representation for each population.
How is Female BAME defined? Which demographic categories are included, and which are not?
On this site, we use these category labels for UK data: Female and Female BAME.
Female BAME represents the intersectionality of simultaneously identifying as female and identifying as BAME. The obstacles, discrimination, inequity, and everyday lived experience of those within this intersection of gender and ethnicity are different to those who are white and female; the overlapping identities create different contexts and sets of experiences, indeed, different ways to be ‘other’, to those who are white and female. For the fields considered on this site, the intersectionality of gender and race has been proven to be a critical factor in participation, feelings of belonging, and persistence. However, most studies on the intersectionality of gender and ethnicity in STEMM fields have occurred outside the UK. This project is one of the first to quantify this kind of intersectionality in the UK and to begin questioning why some departments are more inclusive than others.
Useful sites:
https://aip.scitation.org/doi/pdf/10.1063/1.5110070
https://www.jstor.org/stable/10.1525/sp.2005.52.4.593?seq=1
Where do these data come from?
Data on bachelors degree attainment are calculated from the Higher Education Statistics Agency (HESA)
Athena Swan: An international charter designed to help institutions meet gender equity goals
Athena Swan is a voluntary membership charter designed to encourage and recognise institutional commitment to improving gender inequality in science, technology, engineering, mathematics, and medicine. It was established in 2005. Only those who are members are eligible to apply for awards. The field of medicine made membership a priority when, in 2011, the Chief Medical Officer made silver awards a prerequisite for funding applications. In all other fields, membership in the charter, and awards given to institutions or departments represent the progress and effort made towards gender equity. Athena Swan is equally committed to inequality in other forms, such as the underrepresentation and discrimination faced by other genders and trans peoples. The principles of the charter are:
- We acknowledge that academia cannot reach its full potential unless it can benefit from the talents of all.
- We commit to advancing gender equality in academia, in particular, addressing the loss of women across the career pipeline and the absence of women from senior academic, professional and support roles.
- We commit to addressing unequal gender representation across academic disciplines and professional and support functions. In this we recognise disciplinary differences including:
- the relative underrepresentation of women in senior roles in arts, humanities, social sciences, business and law (AHSSBL)
- the particularly high loss rate of women in science, technology, engineering, mathematics and medicine (STEMM)
- We commit to tackling the gender pay gap.
- We commit to removing the obstacles faced by women, in particular, at major points of career development and progression including the transition from PhD into a sustainable academic career.
- We commit to addressing the negative consequences of using short-term contracts for the retention and progression of staff in academia, particularly women.
- We commit to tackling the discriminatory treatment often experienced by trans people.
- We acknowledge that advancing gender equality demands commitment and action from all levels of the organisation and in particular active leadership from those in senior roles.
- We commit to making and mainstreaming sustainable structural and cultural changes to advance gender equality, recognising that initiatives and actions that support individuals alone will not sufficiently advance equality.
- All individuals have identities shaped by several different factors. We commit to considering the intersection of gender and other factors wherever possible. (Athena Swan Charter | Advance HE, 2020)
Useful sites:
https://www.advance-he.ac.uk/equality-charters/athena-swan-charter
Juno
Project Juno is a voluntary membership initiative through the Institute of Physics designed to recognise and support physics departments, schools, institutes and organisations that are working towards a gender-inclusive culture.
Project Juno is a reciprocal award to Athena Swan, focussing solely on physics. Like Athena Swan, participation is voluntary; participation shows that the department is actively working towards both gender equity and best practices for students, faculty, and staff. Project Juno tries to emphasise eradicating those practices from published literature that appear to effect female participation, persistence, and progression in physics. The principles are:
1. A robust organisational framework to deliver equality of opportunity and reward
1.1. Establish organisational framework
- 1.1.1. Evidence of senior management commitment
- 1.1.2. Effective consultation, communication, monitoring, evaluation and reporting mechanisms
- 1.1.3. Clear accountability for implementation and resources allocated (time and money)
1.2. Monitoring and evidence base
- 1.2.1. Monitor over time, quantitative data by gender. Include all student admissions and performance. All staff applications, shortlists, appointment and promotion, looking at the proportion of women at each stage
- 1.2.2. Obtain qualitative data from staff
- 1.2.3. Identify any discrepancies in gender representation and/or progression and identify factors that might be causing them
2. Appointment and selection processes and procedures that encourage men and women to apply for academic posts at all levels
2.1. Ensure that processes and procedures are fully inclusive
- 2.1.1. Ensure career breaks are taken into consideration
- 2.1.2. Gender awareness included in training for all staff who interview
- 2.1.3. Provide induction for all new staff, including research assistants, on appointment
2.2. Take positive action to encourage under-represented groups to apply for jobs
- 2.2.1. Monitor applications, shortlists and appointments, looking at the proportion of women (internal and external) at each stage
- 2.2.2. Identify any discrepancies and investigate why this might be the case, taking action as necessary
3. Departmental structures and systems which support and encourage the career progression and promotion of all staff and enable men and women to progress and continue in their careers
3.1. Transparent appraisal and development
- 3.1.1. Appraise all staff, including researchers and PDRAs
- 3.1.2. Mentoring scheme in place with training and guidance available for both mentors and mentees
- 3.1.3. Ensure all staff, including PDRAs, have access to impartial career guidance
3.2. Transparent promotion processes and procedures
- 3.2.1. Ensure promotions process is transparent and fair to all staff at all levels, including those who have had a career break
- 3.2.2. Ensure all staff are aware of promotion criteria and process and the support available to them throughout the process
- 3.2.3. Take steps to identify and encourage potential candidates for promotion
4. Departmental organisation, structure, management arrangements and culture that are open, inclusive and transparent and encourage the participation of all staff
4.1. Promote an inclusive culture
- 4.1.1. Ensure departmental processes, procedures and practices are fully inclusive
- 4.1.2. Gender awareness included in the training for all staff and demonstrators
- 4.1.3. Promote inclusive social activities and other opportunities for mutual support and interaction
- 4.1.4. Use positive, inclusive images in both internal and external communications
- 4.1.5. Encourage and support female seminar speakers
4.2. Transparent work allocation model
- 4.2.1. Recognise the full range of types of contribution and departmental role, including administration, welfare and outreach activities
- 4.2.2. Ensure all staff are aware of the criteria used to develop the model and that the allocation is transparent
5. Flexible approaches and provisions that enable individuals, at all career and life stages, to optimise their contribution to their department, institution and to SET
5.1. Support and promote flexible working practices
- 5.1.1. Clear support from Head of Department for flexible and part-time working
- 5.1.2. Consistently applied policy on part-time and flexible working
- 5.1.3. Promote the benefits of flexible working for both men and women, particularly for those with caring responsibilities
- 5.1.4. Explicit support for those returning from career breaks or maternity leave
- 5.1.5. Encourage take up of shared parental, paternity and other caring leave.
6. An environment where professional conduct is embedded into departmental culture and behaviour
6.1. Ensure that all staff and students are aware of expected professional conduct.
6.2. Address bullying, harassment and misconduct
- 6.2.1. Ensure all staff and students are aware of how complaints of bullying, harassment or other misconduct will be dealt with through an enforceable formal policy.
- 6.2.2. Ensure there is a transparent reporting mechanism within the department to address any complaints.
Useful sites:
https://www.iop.org/about/IOP-diversity-inclusion/project-juno
https://www.iop.org/about/IOP-diversity-inclusion/Project-Juno/current-Juno-award-holders
https://www.iop.org/sites/default/files/2020-07/Juno-evaluation-summary
Which institutions are included, and what are the inclusion criteria?
All institutions which offer an IOP accredited course in physics and astronomy are included in the physics and astronomy analysis. For the maths and statistics category, and the computer science category, all universities are included which, according to UCAS, offer a course in these disciplines.
Why isn’t my university displayed on this graph?
Your university is on this graph if they offer an IOP accredited course in physics or astronomy, or a course in mathematics, statistics, or computer science as indicated by UCAS. If your university is not displayed on this graph, it may be because your course is accredited through another institution.