Transparency in data collection, use, analysis, and retention is imperative, and a legal requirement, when we are conducting research. The Code of Practice for Statistics emphasises the importance of transparency about processes, methodology and content. Transparency is particularly important when communicating the inclusivity of data. By being open and honest about our goals for research and statistical projects, including their aims and motivations, and communicating this information in a clear and understandable way, we can better consider how projects should be approached to maximise inclusivity and build trust with relevant communities and stakeholders.

Transparency is particularly important when engaging with under-represented groups or those at greater risk of disadvantage. This may range from direct engagement and the collection of primary data from groups who may be more cautious regarding the use of their data, to carefully considering and clearly communicating the inclusivity of existing data sources and resultant analysis. Ensuring that the motivations of any work related to these groups (i.e., how and why their data is being used), alongside the benefits that it aims to provide, are clearly stated can help to develop trust and positive engagement. However, these benefits should be realistic and considered from the point of view of the groups that you are engaging with – what may be considered beneficial to you as a researcher or statistician may not be considered beneficial by those who are the focus of your work. It is also important not to make over-inflated claims or promises about the impacts that the work will have.

When using data from administrative sources, it is important to be transparent about the strengths and limitations of datasets with regard to inclusivity. This can be achieved by understanding how the data has been collected, identifying errors, uncertainty and bias and clearly outlining what efforts have been made to address and mitigate these risks. In some cases, there may be little information available from data providers on the context of data collection, so transparency regarding what efforts have been made to understand the inclusivity of the data is necessary. Being transparent about how administrative data is used can also help to build trust and confidence with communities around the use of public data for research and statistical purposes, and this needs to be clearly articulated to reach all audiences.

A lack of transparency in your work may also have implications for the reproducibility of your data, which is important in maintaining trust from both the communities that are the focus of your work and the wider analytical or academic community. Being transparent about the way in which data is used, and how data collection and analysis is carried out, allows others to verify the findings of a project via replication.

By being transparent about the strengths and weaknesses of our methods, we can ensure that the inclusivity of current data sources and outputs is clearly documented and identify where further improvements need to be made regarding the adequate consideration of under-represented groups, those at risk of greater disadvantage, and protected characteristics, within research and statistics.

Finally, wherever possible, data that is collected on different groups should be made available and accessible, as long as it is not disclosive, to support further analysis and understanding of the experiences of diverse groups across society.

Aspects to consider for transparency and inclusivity

  • If possible, develop a social contract for research participants to help improve clarity about what people can expect from their engagement or involvement in your work.
  • All parties interacting with projects should be able to ask questions and receive answers to these questions in a timely manner. Not only does this help build trust, but it could also have positive implications for the inclusive nature of your work, as others may be able to identify potential improvements and highlight processes that could be more inclusive.
  • Project decisions should be clearly documented in a way that allows others to understand the rationales and implications of these decisions.
  • An explanation of the outputs and recommendations arising from the project should be made accessible soon after the work is complete. It may be beneficial to think about the best way that this can be delivered to different audiences. For example, a face-to-face workshop may be beneficial in some contexts to allow different groups to better engage with, and understand, your findings rather than a written summary.
  • Consider the way in which you communicate with different communities to ensure that you are getting your message across in the best way and in a manner that enables diverse audiences to understand the aims and motivations behind your work. This could involve applying Respondent Centred Design to your project so that the needs of communities are met by using suitable and accessible methods to deliver messages about their involvement, helping to build trust.
  • If collecting primary data, gatekeepers who represent a trusted person within your community of interest can be utilised as mediators for accessing others. They may also be useful in helping to build rapport with community members.
  • If using secondary data (e.g., administrative data), inclusivity issues and how these have been addressed and mitigated should be clearly documented to ensure transparency around the data’s use.
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