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Segmentation and Code Co-occurrence Accumulation: Operationalizing Relational Context with Stanza Windows

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Advances in Quantitative Ethnography (ICQE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1785))

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Abstract

Depending on analytical goals and techniques, qualitative data may be coded and segmented to investigate code or code co-occurrence frequencies. As codes are relevant aspects of data vis-à-vis the topic of inquiry, segments are meaningful divisions of those data. To explore various modes of segmentation, their underlying assumptions, and effects on potential models, the framework and terminology of Epistemic Network Analysis was employed as an analytical tool where coding and segmentation both contribute to data visualization. Three operationalizations of segmentation are elaborated: moving, infinite, and whole conversation stanza windows and demonstrated through instances where each of these may be applicable to data.

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Notes

  1. 1.

    Some researchers use the term “utterance” to only describe the lowest level of segmentation in textual data, but here we refer to utterance as the smallest codable segment in any type of qualitative data.

  2. 2.

    Codes are always numerical; whether they are represented as binary, continuous, or scale is based on the analysis of the data and/or the output of the coding analysis.

  3. 3.

    Note that any piece of metadata can potentially serve as segmentation.

  4. 4.

    For whole conversation stanza windows, there may not be a referent line, see Sect. 3.

  5. 5.

    Stanza windows are examined in their “backward-facing” variations: co-occurrences are computed from a referent line to lines that precede it, as opposed to subsequent lines.

  6. 6.

    https://www.jubileemedia.com.

  7. 7.

    https://www.softwhiteunderbelly.com; caution: contains language that may be upsetting.

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Acknowledgements

This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 101028644, as well as from University Fund Limburg/SWOL. The opinions, findings, and conclusions do not reflect the views of the funding agency, cooperating institutions, or other individuals. Thank you to Andrew Ruis for his insights on this paper.

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Authors and Affiliations

  1. Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands

    Szilvia Zörgő

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  1. Szilvia Zörgő
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Correspondence to Szilvia Zörgő .

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Editors and Affiliations

  1. University of Oslo, Oslo, Norway

    Crina Damşa

  2. Drexel University School of Education, Philadelphia, PA, USA

    Amanda Barany

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Zörgő, S. (2023). Segmentation and Code Co-occurrence Accumulation: Operationalizing Relational Context with Stanza Windows. In: Damşa, C., Barany, A. (eds) Advances in Quantitative Ethnography. ICQE 2022. Communications in Computer and Information Science, vol 1785. Springer, Cham. https://doi.org/10.1007/978-3-031-31726-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-31726-2_11

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  • Online ISBN: 978-3-031-31726-2

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