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Topic 1: Collecting and Analysing Data

On this page you will find guidance on collecting and analysing data for the transformed UK Athena Swan Charter.

On this page you will find guidance on the following:

  • Why collect equality data? 
  • What data should we collect for Athena Swan? 
  • How do we collect data? 
  • Data gaps and challenges  
  • Data protection and data disclosure 
  • How do we analyse data to identify gender equality issues? 
  • Using data to analyse intersectional inequalities 
  • Further help and support 

Why collect equality data?  

Collecting data is integral to equality, diversity and inclusion work as it allows us to identify gaps and barriers to achieving equality as well as differential experiences or outcomes, inform interventions and evaluate their success, and measure progress towards greater equality and inclusion.  Data also helps us to communicate and advocate for change and to recognize and reflect on inequality, giving visibility and voice to minoritized groups and experiences.  

The idea of informing interventions is key to Athena Swan and in your Athena Swan application, and you will use your data to inform your action planning and your work towards improving gender equality in your institution, department, or directorate.  

What data should we collect for Athena Swan?  

In Athena Swan, 'gender equality' is used as an umbrella term for equality work related to legal protections around gender reassignment, pregnancy and maternity, and sex as well as broader equality work relating to gender identity, trans inclusion and caring responsibilities.  

In your self-assessment, quantitative and qualitative data are used to identify key gender equality issues in your organisation. For the transformed Charter, mandatory quantitative data requirements have been specified for each type of Athena Swan applicant and are outlined in the relevant applicant information packs.

These data sets have been selected based on well-documented gender inequalities in the sector, to enable you to understand patterns of representation across your staff or student population, to identify key areas of under-or over-representation or biases within your unit from a gender equality perspective.  Many institutions will already be collecting at least some of this data for internal monitoring and/or external reporting purposes.  

From September 2024 submission round departmental applicants will also be asked to use the Athena Swan departmental Culture Survey to understand the inclusivity of their culture. For more information about the departmental culture survey, see the departmental information pack.

In addition to these mandatory data for Athena Swan, you may choose to collect other data in line with your own gender equality context and priorities.

How do we collect data? 

When planning your data collection, first, think about what relevant data is available to you from central human resources or other data teams. Think about what you already know, what data you already have access to, who holds relevant data and what format it is in as this will help you plan realistic timescales for your data collection and analysis. Remember that you will need population data over several years.    

Once you have mapped the data already available, think about what else you want to know.  

  • Are there any gaps related to the mandatory data sets?   
  • Is there additional quantitative or qualitative data that you wish to collect to inform your gender equality priorities?  
  • How will you consult with your community and how will staff and student views inform your application?  
  • When will you run the departmental culture survey if you are planning to do so?  

Then consider the timing of any data collection, remember that new applicants need a minimum of 3 years’ data. To monitor trends and report on progress, you will need to repeat surveys at intervals. Consider the timing of any surveys around any existing data collection and other deadlines (e.g., marking) to avoid consultation fatigue and ensure good response rates.   

Certain groups may also have specific accessibility needs. Ensure that data collection takes these into account and provides accessible options for providing information. For example, an online survey may need to have adjustments in place for those who are visually impaired. Some staff may not be desk-based or have easy access to IT and so may need access to paper surveys rather than online ones.

Once you have determined what data needs to be collected, it is worth assigning responsibilities across your self-assessment team (SAT), bearing in mind individuals’ expertise and capacity. You may want to consider recruiting someone with expertise in data analysis for your SAT.  

Tips from previous charter applicants on retrieving data: 

  • Contact your HR or data teams early to understand what is available and how to work with them –ideally a year to18 months ahead of your submission date 
  • Agree on who and what needs to be captured in your data before making any requests. Consider what characteristics you want to disaggregate the data by (for example, sex, grade, or contract type). The more clarity you can give, the more efficient the process of requesting the data is likely to be
  • Your data or HR teams may have a template for making data requests, but if not, it is worth making one
  • Keep track of all the data requests that you make and ensure requests are issue from one point of contact in the team
  • Because you will be making requests from offices or individuals across your institution and may need to make multiple requests, it is important to build relationships with key people – and to thank them for their time and help
  • If you have difficulty retrieving data, you may need to enlist support from the institution’s most senior staff. This will help to indicate that retrieval of this data, and its analysis, is a priority. 

Data protection and data disclosure 

When collecting and analysing your data, it's important to treat it sensitively and work in line with data protection regulations. This will protect your staff and students’ confidentiality and ensure you comply with the law. Consult with your organisation’s data controller or data protection officer to ensure that you are aware of organisational policies and seek advice on how that will affect your work. Ensure the self-assessment team are familiar with any policies or guidelines and their responsibilities within them.   

When designing surveys or other forms of data collection, be clear about what the data is for, how you will store and analyse it and who will see it. Informed consent is important and will help you retain the confidence of those whose data you are using. 

Risks around data protection and confidentiality are reduced when data is anonymized – so wherever possible, work with anonymized data or limit the number of people who have access to data that can identify individuals.  Data that can identify individuals counts as ‘personal data’ under the law and needs to be processed in line with General Data Protection Regulations.  

Ensuring you are clear about the data protection measures in place - including who will have access to what data and to what extent it will be anonymous - will give confidence to staff and students and encourage disclosure. Improvements in data disclosure are also more likely if you communicate regularly with staff and students your commitment to gender equality issues, by providing feedback on survey results and actions planned or taken to address issues identified.   

Other measures you can consider to ensure confidentiality and encourage disclosure include: 

  •  using external facilitators to conduct interviews or focus groups 
  • limiting any sensitive data analysis to a subgroup of your self-assessment team so that information is not shared with everyone  
  • asking your SAT to sign non-disclosure or confidentiality agreements 
  • considering rounding strategies, such as the Higher Education Statistics Agency (HESA) rounding strategy 
  • not quoting results for areas that have fewer than 5 responses to help safeguard the identity of individual staff. 

Which of these measures is appropriate for your data collection and analysis will depend on how your data is collected and what your organisational policies and procedures say about data protection.

Further guidance on encouraging disclosure is available here.

Data gaps and challenges 

If you find you have missing data, data gaps or very low response rates resulting in small numbers then you should explain why in your narrative. Appropriate action could be included in the action plan to address this gap going forward.   

When dealing with equality data, you may have to deal with small numbers of individuals perhaps because a group is not well represented in your institution, or because your department is small. This can pose challenges for your SAT and you might worry about the significance of your findings.  

We advise not to ignore your findings but to treat them with caution and acknowledge the limitations in your Athena Swan application. You might want to aggregate numbers over multiple years or grades to see if consistent patterns emerge. If you have the expertise within your SAT or institution, you might also consider using statistical analysis to test the significance of your findings, but this isn't necessary for Athena Swan.  

Small numbers may also limit the possibility of an intersectional analysis. For this reason, it’s not a requirement for departments and directorates to provide quantitative data on intersectional inequalities. Instead, you may consider using qualitative data or knowledge of the wider sector or university trends to help you explore the experiences of those with intersecting characteristics.  

Working with Data provides further guidance and tools including on small numbers.  

How do we analyse data to identify gender equality issues?  

In undertaking your data analysis, it is useful to approach your data with a curious mindset. Look for trends, discrepancies, and biases in relation to gender equality and investigate the possible reasons for these. When analysing the mandatory data sets, start by asking questions that will help your SAT to identify issues, such as: 

  • Where do you see issues of under- or over-representation of women (e.g. in particular contract types, grades, or job families)?   
  • How does representation change along the student and staff pipeline? 
  • Are women and men equally likely to apply for recruitment or promotion, across roles or grades? Are they equally likely to be successful?  

Asking these or similar questions will help you to identify issues of under-representation or occupational segregation, which might have implications for gender equality in pay and progression, for example, or for the way a team functions or departmental culture if there is a lack of diversity in management or senior leadership.    

For an overview of the context of equalities in your institution, look for obvious trends and patterns. If longitudinal data is available, then it can be helpful to look at how patterns of data have evolved over time.  

External benchmarking of your population data against data from relevant other departments or institutions in the sector can help to contextualise your own data, as well as to identify comparable institutions who may have a good practices that you could consider.

There is more information about benchmarking including data sources here.

As you progress with your analysis, it can be useful to ask why any inequalities exist, what the underlying barriers are and what can be done about them:  

  • Why are some groups underrepresented, or not progressing?  
  • Does your staff feedback or culture survey data offer insights into any trends or issues you have identified?  
  • How is your organisational culture affecting the trends or issues you have identified? 
  • How do policies affect the trends or issues you have identified?  
  • Do policies, or processes, work equally well for different groups, including trans and non-binary staff?  
  • What can we do to accelerate progression, or improve outcomes?  

Asking yourselves these questions will help you understand different experiences and outcomes as well as identify potential priorities and actions. Having a diverse and representative SAT will enable you to analyse your data from different angles and viewpoints.  

Qualitative data is important in your assessment, particularly to understand differences in experience linked to gender equality.  For example, do free text responses to staff surveys or focus group discussions give insights into why one group is more satisfied or having a better experience than another?  More in-depth guidance on analysing qualitative data can be found here.

In order to achieve the richest insights into your gender equality challenges, aim to triangulate data from different sources. Joining the dots between the trends in population data you collect, your analysis of culture survey or similar data, qualitative feedback from free text comments in surveys, or consultations on policies, for example, will help you to understand why something is the case and may suggest what can be done.  

If the reasons for gendered patterns you are observing are not clear, your SAT may identify a need for further targeted data collection or analysis in order to investigate a particular issue you've identified, or a gap in current understanding. We recommend starting data collection and analysis as early as possible in order to be able to take an iterative approach to your assessment.  

Using data to evaluate intersectional inequalities  

Intersectional inequalities need to be considered as part of gender equality work so that our actions promote gender equality for everyone, and don’t inadvertently default to benefitting white women, for example. 

An example of intersectional inequality in the UK Higher Education system is the low numbers of Black, Asian and minority ethnic women who are professors compared to either white women or Black, Asian and minority ethnic men. The intersection of ethnicity and gender means that Black, Asian and minority ethnic women face unique barriers to progression in higher education and are significantly under-represented amongst our most senior academics.  

To evaluate intersectional inequalities, you could choose to analyse existing data sets, such as the mandatory quantitative data sets or the culture survey, with an intersectional lens. You could also build an intersectional analysis into any consultation you have planned with staff.   Intersectional data trends in the UK are considered in Advance HE’s equality in higher education statistical reports, which you can use to inform or contextualise your analysis. 

Analysis of qualitative data collected for example through focus groups, is particularly powerful for evaluating intersectional inequalities because it provides rich, contextualised information that aids in understanding differences in the lived experiences of people as they work or study in higher education and research institutions.  

Further information on intersectional approaches to equality research and data can be found here.

Further help and support 

You can find further useful information in our Data-related queries in the FAQs section of the transformed Charter webpages.   

If you have further questions regarding data requirements for your Athena Swan application please contact the Athena Swan team on