Written by Francisco Espinoza

Recognising diversity in sexual orientations and gender identities has led to more inclusive approaches to data collection.  Institutions have made genuine efforts to acknowledge these diverse forms of identity. Yet for many LGBT+ people, these efforts involve significant trade-offs. Lives are fluid, dynamic, and rarely fixed, while our data categories are predefined and  fixed in time and space. Coming to understand one’s own sexual orientation or gender identity can be a long, personal process;  one that does not always align neatly with the rigid categories used in datasets.

We often like to imagine our data as stable and independent from the messy realities of lived experience. But many LGBT+ lives  resist this static framing. Not everyone is “out” in every context. If we are not out to our employers, are we being forced to  reveal information we would prefer to keep private? Does a bisexual person in a different-sex relationship stop being bisexual  for a survey? If someone feels uncertain or conflicted about their gender identity, how should they report? These tensions  create a mismatch between our expectations of what data should reveal and what it can realistically ‘capture’.

To explore these issues, we invited Dr Kevin Guyan, Chancellor’s Fellow at the University of Edinburgh, to discuss themes from  his book Rainbow Trap. His work examines how inclusive data practices unfold across six areas: crime recording, dating apps,  border controls, cultural industries, fitness activities, and workplace EDI initiatives. In a 40-minute presentation followed by a  Q&A session, we had the opportunity to engage and discuss these challenges.

The event was organised for students in Tools and Techniques of Applied Quantitative Methods, an MA-level course where  students work intensively on data carpentry, applied quantitative data analysis, and communication for both academic and  general audiences. The session was open to staff and postgraduate students, and the discussion was dynamic.

Dr Guyan’s talk offered a shift in the perspective we use to discuss these issues in the lecture, with a chance to pause and reflect on the conceptual foundations of the data practices we so often take for granted. Participants reflected on issues ranging from disclosure and workplace data regulations to navigating classification systems embedded in institutional contexts,  including government, academia, and industry.

This event served as an invitation to rethink how we approach data collection, production, and analysis. As quantitative  researchers -whether in academia, government, or the private sector- we must cope with what inclusive data practices require. Ultimately, additive approaches, such as simply adding more categories or boxes, may not be sufficient. Instead, we need to  consider the deeper implications for data design, implementation, and governance. Only then can our data practices begin to reflect the complexity of the lives they aim to represent.

It was also an opportunity to bring these conversations into dialogue with our department’s EDI lead, Dr Jamie Hagen, who  highlighted the relevance beyond this single course to our broader research practices as scholars, supervisors, and instructors.  In doing so, we connect an important academic concern with the everyday realities of our work.

The discussion set an encouraging precedent. It reminded us that inclusion is not only about counting more voices, but about  ensuring those voices actively shape our conceptual frameworks, influence our operational decisions, and have a seat at the table when institutional choices are made. The shift from merely recognising diversity to embedding it in our analytical  foundations has the potential to transform how we teach, research, and design our data.

As we continue developing our curriculum, we plan to include these ideas as accessible and essential readings for future  cohorts. By including these conversations into our teaching, we help prepare the next generation of researchers with the  awareness and tools needed to build more thoughtful, responsive, and genuinely inclusive data practices. In this sense, the  impact of the event extends well beyond a single lecture. It is a commitment to recognise complexity not as a challenge to  overcome, but as an opportunity to create better and ethical approaches to data for the world we study.