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

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Bibliographic Details
Title: ADQDA: A Cross-device Affinity Diagramming Tool for Fluid and Holistic Qualitative Data Analysis
Authors: Liu, Jiali, Eagan, James, R
Contributors: Design, Interaction, Visualization & Applications (DIVA), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom Paris (IMT)-Télécom Paris, Institut Mines-Télécom Paris (IMT)-Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Institut Mines-Télécom Paris (IMT)-Télécom Paris, Institut Mines-Télécom Paris (IMT)-Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris), Institut Polytechnique de Paris (IP Paris), Département Informatique et Réseaux (INFRES), Télécom ParisTech
Source: ISSN: 2573-0142.
Publisher Information: CCSD
Association for Computing Machinery (ACM)
Publication Year: 2021
Subject Terms: affinity diagramming, qualitative data analysis, sensemaking, cross-device interaction, multi-surface interaction, collaboration, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
Description: International audience ; 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.
Document Type: article in journal/newspaper
Language: English
DOI: 10.1145/3488534
Availability: https://imt.hal.science/hal-03370011
https://imt.hal.science/hal-03370011v1/document
https://imt.hal.science/hal-03370011v1/file/iss21-adqda.pdf
https://doi.org/10.1145/3488534
Rights: info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.53A9DA6C
Database: BASE
Description
Abstract:International audience ; 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.
DOI:10.1145/3488534