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Journal Articles Proceedings of the ACM on Human-Computer Interaction Year : 2021

ADQDA: A Cross-device Affinity Diagramming Tool for Fluid and Holistic Qualitative Data Analysis

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Abstract

Affinity diagramming is widely applied to analyze qualitative data such as interview transcripts. It involves multiple analytic processes and is often performed collaboratively. Drawing on interviews with three practitioners and upon our own experience, we show how practitioners combine multiple analytic processes and adopt different artifacts to help them analyze their data. Current tools, however, fail to adequately support mixing analytic processes, devices, and collaboration styles. We present a vision and prototype ADQDA, a cross-device, collaborative affinity diagramming tool for qualitative data analysis, implemented using distributed web technologies. We show how this approach enables analysts to appropriate available pertinent digital devices as they fluidly migrate between analytic phases or adopt different methods and representations, all while preserving consistent analysis artifacts. We validate this approach through a set of application scenarios that explore how it enables new ways of analyzing qualitative data that better align with identified analytic practices. CCS Concepts: • Human-centered computing → Interactive systems and tools; • Information systems → Collaborative and social computing systems and tools.
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Dates and versions

hal-03370011 , version 1 (07-10-2021)

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Jiali Liu, James R Eagan. ADQDA: A Cross-device Affinity Diagramming Tool for Fluid and Holistic Qualitative Data Analysis. Proceedings of the ACM on Human-Computer Interaction , 2021, 5 (ISS), pp.19. ⟨10.1145/3488534⟩. ⟨hal-03370011⟩
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