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Topic guide 2: collecting and analysing data

On this page you will find guidance on collecting and analysing data for the Athena Swan Ireland Charter 2021 framework.

The Athena Swan Ireland framework requires applicants to undertake transparent self-assessment processes to ensure priorities, interventions, and actions are evidenced-based and inform continuous development. These processes are integral to meeting the criteria for an award.

Evidence-based recognition of the issues and opportunities facing applicants requires the collection and analysis of quantitative and qualitative data. As part of this, applicants collect equality monitoring data to measure, understand, and report on challenges and progress.

The topic guidance on ‘Collecting and analysing data’ will provide you with information on the following:

  • The rationale for collecting equality data
  • Collecting equality data
  • Encouraging disclosure
  • Analysing and reflecting on equality data
  • Presenting data   

The rationale for collecting equality data

Firstly, “all public bodies in Ireland have responsibility to promote equality, prevent discrimination and protect the human rights of their employees, customers, service users and everyone affected by their policies and plans. This is a legal obligation, called the Public Sector Equality and Human Rights Duty, and it originated in Section 42 of the Irish Human Rights and Equality Act 2014.

Effective implementation of the Duty – like effective implementation of the Athena Swan Ireland charter framework – requires an evidence-based approach. This involves identifying and collating relevant data and information related to the grounds in equality legislation.

In order to address inequalities in the higher education community we must identify and understand them. Collecting data can help us get to know the inequalities within our institutions better. It can highlight different experiences and help us to target support and resources. Data helps us to recognise existing barriers and can help to identify information gaps. It is through collecting and analysing data that we can fully understand the current picture of our institution and identify what needs to change.

After identifying what needs to change, data can then help us to evaluate the impact of interventions and monitor equality progress. This in turn creates a record of the history of equalities within our institution.

Collecting equality data  

The data requirements are outlined in the relevant applications forms, and are underpinned by the Athena Swan Ireland charter principles. By participating in the Ireland charter framework, participants strive for impactful and sustainable gender equality work and seek to build capacity in evidence-based equality interventions across the equality grounds enshrined in Irish legislation. Additionally, intersectional inequalities must be accounted for in the development of effective equality analysis and actions.

The Ireland charter framework requires the collection and analysis of quantitative and qualitative data.

Quantitative data is expressed through numbers. Institutions will typically be collecting quantitative data on a large scale. For example, institutions often produce data through HR systems, staff surveys, admissions forms, and student evaluations.

Qualitative data is expressed through words. Methods of qualitative data collection include interviews, focus groups, or questionnaires, and generally this information would be presented as quotes, case studies, or in narrative. More information on analysing qualitative data can be found here.

Different types of data are generally used for different purposes. Each is valuable in its own way. Quantitative data can be used to show trends, prevalence, and patterns. As it can include a large sample number, quantitative data can provide a strong representation of the population you are looking at. In contrast, qualitative data can provide a nuanced and detailed look at your environment and communities. It can be used to explore concepts, experiences or opinions.  

It is important for applicants to understand that improving quantitative numbers is crucial for promoting equality, visibility, and equal representation and opportunity. However, parity alone will not necessarily remove the systemic barriers to gender equality. Qualitative data is a key tool for investigating if barriers exist and what actions could be introduced to address them. It also can provide important insights into contexts where there is gender balance or parity.

It may be appropriate to vary the methods and approaches you use to collect data on different equality grounds, particularly when small numbers are involved, or if there are concerns about anonymity. Some data may be collected as part of HR systems and processes, whereas other data may be collected as part of staff consultation. For example, you might collect data on all staff through self-reporting in institutional data systems to understand representation across roles and grades. You may also explore the experiences and perceptions of particular staff cohorts through consultation exercises. Guidance on data derived from consultation requirements can be found in Topic Guide 3: Consulting with your community.

Remember that language relating to a lot of identities is evolving. You will likely need to review the language used in data collection regularly to ensure it is up to date. Open approaches that allow people to self-identify may be advisable. 

Alignment with Higher Education Authority (HEA) data

The Higher Education Institutional Staff Profiles by Gender collate gender-disaggregated data from the higher education sector in Ireland. These profiles provide information on key indicators that contribute to the assessment of gender equality in Irish institutions. The HEA has also taken steps to collect data on staff ethnicity. Institutions may wish to align their Athena Swan Ireland application data with their returns to the HEA, where appropriate.

Future data collection

The purpose of data collection is to inform change and ongoing work on equalities. Continuing data collection is an important part of assessing the impact of interventions. While you might initially be collecting data to inform an Athena Swan Ireland application it is important to look to the future with your data collection. Ask yourself the following questions:

  • Is this data collection replicable if it needs to be revisited?
  • Can follow-ups be put in place to assess the impact of any changes?
  • Is the data being used to inform changes effectively?

Continued data collection will allow the success of your equality initiatives to be measured.

Encouraging disclosure

Collecting data on equality grounds can be a sensitive issue and return rates may be low initially. However, over time, in a safe and supportive environment, return rates are likely to increase, and the quality and validity of the data will improve.

Institutions, departments, and professional units should take steps when necessary to support and encourage disclosure. Several factors can influence an individual’s decision to disclose equality information, including:

  • Understanding the relevance of the information sought to the organisation.
  • Availability of information on the purpose, usage and confidentiality of equality information collected.
  • Opportunities to disclose information on an ongoing basis.
  • Understanding the relevance of disclosure for their work or study.
  • The culture of the institution – whether it is perceived to be open and inclusive, or if there are concerns regarding bias or discrimination (direct or indirect), or the transparency of processes and practices.
  • Whether individuals relate to the categories and themes available on equality monitoring forms.

Staff and students are more likely to engage with an equality, diversity and inclusion exercise if they see it as an integrated part of an institution’s strategy for promoting inclusivity and increasing accessibility. Institutions, departments and professional units can take a number of practical steps to increase disclosure:

  • Ensure visible and active support from senior management and trade unions.
  • Make equality, diversity and inclusion commitments visible on campus and in communications via images and text used, and in public forums and events. 
  • Share examples of how the equality information collected has informed action and helped to remove barriers for staff and students.
  • Celebrate achievements to ensure that staff and students feel positive about equality monitoring and confident that it will be of benefit.
  • Include questions that demonstrate the institution’s commitment to understanding the issues affecting particular groups. This may persuade staff of the benefits of disclosure. For example: ‘How well does the institution enable you to meet your religious obligations while at work?’; ‘We want our workplace to be inclusive and welcoming of all staff – is there more we could do to improve your experience?’.
  • Explain clearly why the data is being collected, how it will be used, and who will have access to it, to build a culture of trust and understanding.
  • Provide reassurance that the institution will follow guidelines on data collection, storage and use – including complying with data protection legislation – as some people may be concerned that information could disadvantage them, or encourage discrimination or harassment.
  • Communicate if individuals will be identifiable from the data, if the information will be stored separately from personal details, and if disclosure will lead to further contact from the institution; for example, sharing information about support services or events related to an equality ground.

Analysing and reflecting on equality data

Initial high-level analysis of your data may provide you with an overview of the equality context in your institution. However, in the majority of cases, more complex analysis will be needed. This deeper analysis will reveal more about the nuances of equalities and it is important to approach your data with a critical lens.

For example, an initial analysis might show that fewer women hold senior positions than men. It may not reveal why this is the case. If there is a “why” with no answer, then follow up data collection may need to occur. For this reason, it is useful to plan for multiple layers of staged data collection.


Advance HE recognises that each institution, department and professional unit has different equality challenges and development priorities, and that these priorities should be developed based on an understanding of the local evidence-base and national and global equality challenges in higher education.

To support this contextualisation, the Athena Swan Ireland application forms note when data should be compared with relevant external benchmarking data. Benchmarking with appropriate comparators will provide you with understanding of the scale of the issues and opportunities the institution/department/professional unit is facing.

The benchmarks used will depend on the institutional, departmental, or professional unit context. Example benchmarks include:

  • Sector-wide data from the Higher Education Authority.
  • International data, for example from individual institutions or sub-units, or from sector agencies such as the UK Higher Educations Statistics Agency.  
  • Discipline-specific data, such as those acquired from a professional body, society, or research organisation.
  • Discipline-specific benchmarks collected from similar departments or units in higher education institutions in Ireland or abroad.
  • Data presented in publicly available Athena Swan applications.
  • Irish census data.

Some applicants may find it particularly challenging to identify appropriate external benchmarking data. For example, departments that focus on interdisciplinary research, for which there are very few comparators. In these cases, benchmarking should still be attempted, and it should be explained in the submission why particular benchmarks have been used.


You will be expected to disaggregate and analyse data in a number of ways. Expectations are detailed in the relevant application form. For example, data may be disaggregated by a combination of:

  • Equality grounds, e.g. by gender, presented by male, female and non-binary staff and students, or ethnicity, presented by categories aligned to the Irish census.

Alongside information on:

  • Category of post, e.g. by academic, research, or professional managerial and support staff. Further disaggregation within category of post may be necessary; for example, to capture differences between clinical versus non-clinical staff, or among teaching-only (e.g. fixed-term; hourly paid) staff to understand occupational segregation;
  • Grade, e.g. by postdoctoral researcher, lecturer, senior lecturer, associate professor, professor to understand the academic career pipeline
  • Programme type, e.g. by undergraduate, postgraduate taught, postgraduate research, to understand the student pipeline;
  • By discipline, e.g. looking at differences across degree programmes to understand unequal representation.

Working with small numbers

Dealing with equality data often involves small numbers of individuals. This may be because a group isn’t well represented in your institution, department or professional unit, or because your institution or sub-unit itself is quite small. It is possible to get meaningful results from small samples. You may decide to aggregate some data sets to draw out trends without losing the equality emphasis of the framework. It may also be necessary to choose more qualitative methods to identify issues and opportunities, such as one-to-one interviews or focus groups.

Presenting data

Data should be presented in whichever way applicants feel is most explanatory and appropriate (e.g. tables or graphs), as long as the chosen format clearly highlights trends and these are drawn out in the narrative:

  • Percentages and raw numbers should be presented for all quantitative data (both in figures and within the narrative).
  • Qualitative data should provide relevant detail on the respondent to support analysis (e.g. gender and category of post), while being cognisant of where individuals could be identifiable. Where this is the case, applicants may choose to limit or redact some details.
  • Where data is used to inform a particular action point, the rationale and the actual action point should be embedded in the narrative and cross-referenced to the full action plan.
  • Where data is not available, this should be explained with reasons given (and, in most cases, a relevant action). Applications will not be penalised for only presenting the minimum number of years of data. Check each section of the relevant application form for the exact data requirements for that section.
  • Consider the accessibility of data presentation in terms of the size of the figures and texts, as well as the contrast in chosen colours.