Inclusivity of Data: Key points to consider
Work through the questions below to help you think through some of the key ethical considerations in ensuring inclusivity within your research.
1. Thinking about under-represented groups
Think about which under-represented groups you are considering when addressing inclusivity within your project.
- Ideally, it is better to think holistically and consider as many population groups as possible, rather than addressing just one disaggregational indices or group, depending on the focus and aims of your work.
Think about how your project can maximise the use of inclusive data, either by empowering different people to participate or by assessing inclusivity within existing data sources.
- Do individuals have sufficient understanding, capacity, and mechanisms to share their views?
- Are data sources representative?
- How can these aspects be (a) assessed and (b) addressed in your project approach?
2. Protected Characteristics
Think about how you are considering protected characteristics within the data.
- Are you collecting data directly related to this? Are you clear on the precise concepts that you are collecting data on (i.e., that you are not conflating terms, such as sex and gender identity)? Are you clear on the purpose for which you are collecting this data?
- Is the above documented and communicated clearly?
- Is data collection undertaken in a way that supports all groups to provide this data in a way that meets their needs, following the Respondent Centred Design Framework?
- Are you using harmonised standards in the measurement of protected characteristics?
- Are you collecting any data that may be considered a proxy for any protected characteristics (i.e., attributes that may correlate with protected characteristics)? If so, how are you managing and analysing this data to ensure sufficient data protection, data quality and transparency in how the data will be used and its limitations?
- Have inclusivity issues regarding data collection and quality been identified and addressed appropriately when using administrative data?
Think about how you are supporting inclusivity in the design of your data collection methods, analysis and dissemination of findings, including maximising accessibility and reducing the potential for language, cultural, physical, social or other barriers to limit participation and engagement.