Unpacking the Atom of Identity: Implications from a thematic analysis on demographic data in neurodivergent learning and performance literature

When conducting education research, demographic data helps researchers and educators conceptualize exactly whom they are studying. For instance, when evaluating the efficacy of an assessment tool, collecting demographic data allows researchers to see whether they are oversampling specific populations such as 4-year college students. In qualitative research, demographic data allows researchers to describe participant populations and to contextualize their findings within individuals' identities. It is therefore important that demographic data is collected in a way that is accurate to participant's own identities and does not hyper-aggregate participants into vague or general categories. During the quantitative data analysis process for a literature review on neurodivergent STEM learning and performance, we found discrepancies in how neurodivergent participants' demographic data was collected and represented - that a majority of the literature we sampled from represented neurodivergent demographic data in a hyper-aggregate and/or prescriptive way. In this talk, we discuss the importance of taking an individual and descriptive approach to neurodivergent demographic data collection as it relates to implications and findings. We discuss ways to guide data collection and representation by using a DisCrit lens.