Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

Chapko, Dorota; Rothstein, Pedro; Emeh, Lizzie; Frumiento, Pino; Kennedy, Donald; Mcnicholas, David; Orjiekwe, Ifeoma; Overton, Michaela; Snead, Mark; Steward, Robyn; Sutton, Jenny; Bradshaw, Melissa; Jeffreys, Evie; Gallia, Will; Ewans, Sarah; Williams, Mark; Grierson, Mick

Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first co-analyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learning-based structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by co-researchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed.