Periodic Reporting for period 1 - DACAT (Developing an Abductive Coding Analysis Toolkit)
Periodo di rendicontazione: 2024-03-01 al 2025-08-31
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.
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.
The DACAT Proof of Concept (PoC) project followed a three-step pathway to establish abduction as a robust methodological approach in qualitative data analysis and to create the Abductive Coding Analysis Toolkit (ACAToolkit).
1. Mapping existing practices and tools : A systematic literature review was conducted to map how abductive coding is currently used across disciplines. This produced the first consolidated overview of practices, strengths, and limitations, and identified major gaps in methodological guidance.
2. Toolkit development through collaboration: Building on this mapping, the ACAToolkit prototype was co-designed with key stakeholders – educators, researchers, and data analysts. Two co-creation workshops held in Europe and in the US helped refine its structure and content. The prototype includes three components: A practical guide explaining abductive coding procedures and methodological rationales ; A training module ; A short video introducing abductive coding to a broader audience. When fully developed, these elements will be integrated into a cohesive, widely applicable toolkit suitable across disciplines and methodological traditions.
3. Testing and validation with end-users: The prototype was piloted in four in-situ tests with researchers, students, educators, and professional data analysts. These trials provided empirical evidence on usability, adaptability, and methodological robustness. Feedback from participants will guide the next round of refinements, ensuring the final toolkit combines scientific rigor with practical usability.
Main Achievements of the DACAT PoC : First systematic mapping of abductive coding practices across research fields ; Development of a structured toolkit prototype combining methodological and educational resources, supported by different media ; Empirical validation of the prototype through multi-stakeholder testing in real research and teaching environments ; Consolidation of abduction as a methodologically grounded and operationalizable approach to qualitative data analysis, bridging the gap between theory-building ambitions and today’s data-intensive research practices.
The DACAT PoC strengthens the epistemological foundations of qualitative data analysis and provides researchers with concrete, practical resources for advancing theory-building practices across disciplines.
1. Mapping existing practices and tools : A systematic literature review was conducted to map how abductive coding is currently used across disciplines. This produced the first consolidated overview of practices, strengths, and limitations, and identified major gaps in methodological guidance.
2. Toolkit development through collaboration: Building on this mapping, the ACAToolkit prototype was co-designed with key stakeholders – educators, researchers, and data analysts. Two co-creation workshops held in Europe and in the US helped refine its structure and content. The prototype includes three components: A practical guide explaining abductive coding procedures and methodological rationales ; A training module ; A short video introducing abductive coding to a broader audience. When fully developed, these elements will be integrated into a cohesive, widely applicable toolkit suitable across disciplines and methodological traditions.
3. Testing and validation with end-users: The prototype was piloted in four in-situ tests with researchers, students, educators, and professional data analysts. These trials provided empirical evidence on usability, adaptability, and methodological robustness. Feedback from participants will guide the next round of refinements, ensuring the final toolkit combines scientific rigor with practical usability.
Main Achievements of the DACAT PoC : First systematic mapping of abductive coding practices across research fields ; Development of a structured toolkit prototype combining methodological and educational resources, supported by different media ; Empirical validation of the prototype through multi-stakeholder testing in real research and teaching environments ; Consolidation of abduction as a methodologically grounded and operationalizable approach to qualitative data analysis, bridging the gap between theory-building ambitions and today’s data-intensive research practices.
The DACAT PoC strengthens the epistemological foundations of qualitative data analysis and provides researchers with concrete, practical resources for advancing theory-building practices across disciplines.
The outcomes of the project have strong potential impacts in both the academic and applied research domains:
1. Scientific impact: By consolidating abduction as a methodologically grounded and operationalizable approach, the project strengthens the epistemological foundations of qualitative data analysis and supports theory-building in data-intensive contexts.
2. Educational impact: The ACAToolkit prototype offers concrete, accessible resources that enhance the teaching and learning of qualitative methods across disciplines.
3. Technological impact: The review of QDA software highlights an urgent gap between methodological advances in abductive coding and the tools currently available to researchers. Addressing this gap has the potential to transform the practice of qualitative research by providing more suitable software support for abductive approaches.
For the project results to reach their full potential, several steps are required:
1. Further research and demonstration: Expanding on the current analytical review through a comparative meta-analysis of QDA software and coding practices, in order to assess systematically how different software aligns (or fails to align) with abductive coding.
2. Stakeholder engagement: Conducting interviews and focus groups with researchers, educators, and software developers to assess the novelty, utility, and industrial applicability of a dedicated abductive coding tool.
3. Pathways to software innovation: Determining whether abductive coding can be integrated into existing QDA software through dedicated packages, or whether the architecture of existing tools is incompatible—necessitating the development of a new platform.
The DACAT PoC has consolidated abduction as a practical methodological approach through the ACAToolkit prototype and created new opportunities for its dissemination and uptake. The analytical review of QDA software—although software development was not feasible within the project timeframe—has laid the groundwork for the next phases.
1. Scientific impact: By consolidating abduction as a methodologically grounded and operationalizable approach, the project strengthens the epistemological foundations of qualitative data analysis and supports theory-building in data-intensive contexts.
2. Educational impact: The ACAToolkit prototype offers concrete, accessible resources that enhance the teaching and learning of qualitative methods across disciplines.
3. Technological impact: The review of QDA software highlights an urgent gap between methodological advances in abductive coding and the tools currently available to researchers. Addressing this gap has the potential to transform the practice of qualitative research by providing more suitable software support for abductive approaches.
For the project results to reach their full potential, several steps are required:
1. Further research and demonstration: Expanding on the current analytical review through a comparative meta-analysis of QDA software and coding practices, in order to assess systematically how different software aligns (or fails to align) with abductive coding.
2. Stakeholder engagement: Conducting interviews and focus groups with researchers, educators, and software developers to assess the novelty, utility, and industrial applicability of a dedicated abductive coding tool.
3. Pathways to software innovation: Determining whether abductive coding can be integrated into existing QDA software through dedicated packages, or whether the architecture of existing tools is incompatible—necessitating the development of a new platform.
The DACAT PoC has consolidated abduction as a practical methodological approach through the ACAToolkit prototype and created new opportunities for its dissemination and uptake. The analytical review of QDA software—although software development was not feasible within the project timeframe—has laid the groundwork for the next phases.