Brown, N;
Collins, J;
(2021)
Systematic visuo-textual analysis: A framework for analysing visual and textual data.
Presented at: The Qualitative Report 12th Annual Conference, Virtual conference.
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Abstract
As qualitative research has evolved, researchers now often combine interviews with the production of photographs, artefacts, collages, maps or drawings and the like. However, in practice, the artefacts produced are used to eliciting experiences and stimulating conversations rather than as data, per se, which is often due to the lack of guidelines for how to deal with the artefacts as data in a systematic analytical process. In this article, we present the Systematic Visuo-Textual Analysis, a framework developed to provide much-needed support for qualitative researchers in analysing artefacts in combination with interviews. Drawing on existing frameworks for visual and textual analysis the focus of this framework is to analyse visual and textual datasets separately and in conjunction with one another through several levels of interpretation from noticing descriptive elements and focussing on specific linguistic and artistic elements through to developing conceptual themes. Drawing on examples from our own research, we will demonstrate the practical application of the Systematic Visuo-Textual Analysis.
Type: | Conference item (Presentation) |
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Title: | Systematic visuo-textual analysis: A framework for analysing visual and textual data |
Event: | The Qualitative Report 12th Annual Conference |
Location: | Virtual conference |
Dates: | 12 - 14 January 2021 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.46743/2160-3715/2021.4838 |
Publisher version: | https://nsuworks.nova.edu/tqrc/twelfth/ |
Language: | English |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Arts and Sciences (BASc) |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10119252 |
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