Summary
In qualitative data analysis oriented towards theory-building, many data analysts anchor their methodological approach – explicitly or not – in grounded theory. However, as research in teams develops, handling larger amounts of qualitative data enabled by technological developments, grounded theory’s principles are increasingly at odds with current practices; and analysts of qualitative data are left relying on coding procedures that do not match their realities. Following a burgeoning literature and building on the ERC-funded QUALIDEM project, we argue that shifting to an abductive approach of coding, that combines deduction and induction at different stages of data analysis, provides an epistemology to qualitative research that aims at theory-building. This PoC aims to develop abduction as a method of qualitative data analysis by providing a toolkit for abductive coding – the Abductive Coding Analysis Toolkit (ACAToolkit). The ACAToolkit includes three main components: (1) a practical guide ; (2) an educational program for training; (3) an overview of the potentials and limits of existing QDA software - for abductive coding. The toolkit, thereby, targets several audiences: academics, instructors, data analysts and software developers. Our pathway from research to innovation consists of three steps: (1) mapping the existing practices, and tools of abductive coding in different disciplines through a systematic literature review whose results will be presented in a technical report; (2) developing our abductive coding analysis toolkit through research collaborations and consultancy agreements with stakeholders – educators, software developers and data analysts, synthesized during two cocreation workshops; (3) testing the ACAToolkit with different potential end-users during five in situ tests – with researchers, students, educators, software developers, and data analysts. The toolkit aims to be free, attractive, widely applicable, and with a wide disciplinary range.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101157974 |
| Start date: | 01-03-2024 |
| End date: | 31-08-2025 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
In qualitative data analysis oriented towards theory-building, many data analysts anchor their methodological approach – explicitly or not – in grounded theory. However, as research in teams develops, handling larger amounts of qualitative data enabled by technological developments, grounded theory’s principles are increasingly at odds with current practices; and analysts of qualitative data are left relying on coding procedures that do not match their realities. Following a burgeoning literature and building on the ERC-funded QUALIDEM project, we argue that shifting to an abductive approach of coding, that combines deduction and induction at different stages of data analysis, provides an epistemology to qualitative research that aims at theory-building. This PoC aims to develop abduction as a method of qualitative data analysis by providing a toolkit for abductive coding – the Abductive Coding Analysis Toolkit (ACAToolkit). The ACAToolkit includes three main components: (1) a practical guide ; (2) an educational program for training; (3) an overview of the potentials and limits of existing QDA software - for abductive coding. The toolkit, thereby, targets several audiences: academics, instructors, data analysts and software developers. Our pathway from research to innovation consists of three steps: (1) mapping the existing practices, and tools of abductive coding in different disciplines through a systematic literature review whose results will be presented in a technical report; (2) developing our abductive coding analysis toolkit through research collaborations and consultancy agreements with stakeholders – educators, software developers and data analysts, synthesized during two cocreation workshops; (3) testing the ACAToolkit with different potential end-users during five in situ tests – with researchers, students, educators, software developers, and data analysts. The toolkit aims to be free, attractive, widely applicable, and with a wide disciplinary range.Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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