Abstract
Quantitative ethnographic models are typically constructed using qualitative data that has been segmented and coded. While there exist methodological studies that have investigated the effects of changes in coding on model features, the effects of segmentation have received less attention. Our aim was to examine, using a dataset comprised of narratives from semi-structured interviews, the effects of different segmentation decisions on population- and individual-level model features via epistemic network analysis. We found that while segmentation choices may not affect model features overall, the effects on some individual networks can be substantial. This study demonstrates a novel method for exploring and quantifying the impact of segmentation choices on model features.
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Notes
- 1.
Qualitative data tables contain various kinds of data where rows and columns exhibit ontological consistency. Rows contain the same categories of values (data and metadata), while each column contains one type of information (e.g., age of participants).
- 2.
The method we describe below can be used to compare any two ENA models that use the same units and codes, and thus does not require a gold standard model. We chose to use one here to reduce the number of comparisons made and simplify the presentation of the results. In many cases it may be difficult to justify a gold standard model. However, if one is justified, an alternate approach is to project all other models into the metric space produced by the gold standard model. Such an approach has the advantage of comparing units of analysis along fewer dimensions, rather than making comparisons in a high-dimensional space. We applied both approaches to these data and our results were consistent between them. Here we present only the high-dimensional comparisons to demonstrate the most general approach.
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Acknowledgements
The authors are grateful to collaborator GJY Peters and to research assistants Anna Geröly, Anna Jeney, and Krisztina Veres for their rigorous work in the project that provided our empirical data. This work was funded in part by the National Science Foundation (DRL-1661036, DRL-1713110), the Wisconsin Alumni Research Foundation, and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison. The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.
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Zörgő, S., Swiecki, Z., Ruis, A.R. (2021). Exploring the Effects of Segmentation on Semi-structured Interview Data with Epistemic Network Analysis. In: Ruis, A.R., Lee, S.B. (eds) Advances in Quantitative Ethnography. ICQE 2021. Communications in Computer and Information Science, vol 1312. Springer, Cham. https://doi.org/10.1007/978-3-030-67788-6_6
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