Abstract
Segmentation is a crucial step in analyses of qualitative data where code or code co-occurrence frequencies are of interest. Decisions about how best to segment are inextricably connected to coding decisions, as well as wider analytical goals and research questions. These decisions directly affect resulting models and the interpretations derived from them. However, while there is a wealth of frameworks guiding code development and application, far fewer guidelines exist for segmentation. This paper reports on the development of an initial set of heuristics for the segmentation of monologic data. Using the framework of Epistemic Network Analysis, we demonstrate various approaches to segmentation and show how these segmentation decisions affect models and subsequent interpretations. We argue that segmentation should be aligned with research questions and developed in conjunction with coding, and we offer considerations and techniques for doing so.
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Notes
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https://www.softwhiteunderbelly.com; this data contains language and imagery that may be upsetting for some readers.
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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.
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Zörgő, S., Brohinsky, J. (2023). Parsing the Continuum: Manual Segmentation of Monologic Data. 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_12
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