Abstract
This chapter describes the creative analytic process of transforming quantitative data gathered via an online survey into a graphic novella. The survey, Living and Working in Lockdown: What's Gender Got To Do With It?, focused on the experiences of UK university staff living and working through the first COVID- 19 pandemic lockdown. The graphic novella: Five ‘Survive’ Lockdown is an intertwining of words and images in sequential form (Baetens and Surdiacourt, 2020) and narrates the stories of five fictional female academics during the period of March to September 2020. This revisualisation of data is located between two contrasting philosophical approaches to knowledge, meaning, and the social world: a positivist emphasis on statistical significance and proof derived from quantitative data and an interpretivist approach deploying subjective and situated knowledge to understand social phenomena.
A common refrain I hear when talking to other academics about using graphic or visual elements within research dissemination is ‘But I can't draw so this isn't something I could consider.’ It seems important to clarify that while I have developed a practice of graphic social science, that is, using graphics and/ or visuals to communicate social research (Carrigan, 2017), I am not a trained – or even a particularly competent – graphic artist, nor am I an expert in visual theory or semiotics. I am an interdisciplinary academic, bringing sociological, geographical, and educational theories and concepts to bear on primarily qualitative research into gendered issues in contemporary higher education (HE). Ontologically I am an interpretivist, viewing social phenomena as subjective, plural, and socially constructed. Epistemologically I align with a feminist perspective, which rejects a positivist stance of objective and universal knowledge and argues that how and what we know is shaped by our social location and positionality.
This chapter aims to walk the reader through a specific example of the revisualisation of an online survey data set into a graphic novella. It first contextualises the original survey research (Carruthers Thomas, 2020) and the nature of my own graphic social science practice. It then outlines my motivations for revisualising the survey data in this way, referring to the work of Mannay and McLure, among others.
A common refrain I hear when talking to other academics about using graphic or visual elements within research dissemination is ‘But I can't draw so this isn't something I could consider.’ It seems important to clarify that while I have developed a practice of graphic social science, that is, using graphics and/ or visuals to communicate social research (Carrigan, 2017), I am not a trained – or even a particularly competent – graphic artist, nor am I an expert in visual theory or semiotics. I am an interdisciplinary academic, bringing sociological, geographical, and educational theories and concepts to bear on primarily qualitative research into gendered issues in contemporary higher education (HE). Ontologically I am an interpretivist, viewing social phenomena as subjective, plural, and socially constructed. Epistemologically I align with a feminist perspective, which rejects a positivist stance of objective and universal knowledge and argues that how and what we know is shaped by our social location and positionality.
This chapter aims to walk the reader through a specific example of the revisualisation of an online survey data set into a graphic novella. It first contextualises the original survey research (Carruthers Thomas, 2020) and the nature of my own graphic social science practice. It then outlines my motivations for revisualising the survey data in this way, referring to the work of Mannay and McLure, among others.
| Original language | English |
|---|---|
| Title of host publication | The Handbook of Creative Data Analysis |
| Editors | Helen Kara, Dawn Mannay, Alistair Roy |
| Place of Publication | Bristol |
| Publisher | Policy Press |
| Edition | 1 |
| ISBN (Print) | 9781447369561 |
| DOIs | |
| Publication status | Published (VoR) - 7 Jan 2025 |