Abstract
While quantitative ethnographers have used epistemic network analysis (ENA) to model trajectories that show change in network structure over time, visualizing trajectory models in a way that facilitates accurate interpretation has been a significant challenge. As a result, ENA has predominantly been used to construct aggregate models, which can obscure key differences in how network structures change over time. This study reports on the development and testing of a new approach to visualizing ENA trajectories. It documents the challenges associated with visualizing ENA trajectory models, the features constructed to address those challenges, and the design decisions that aid in the interpretation of trajectory models. To test this approach, we compare ENA trajectory models with aggregate models using a dataset with previously published results and known temporal features. This comparison focuses on interpretability and consistency with prior qualitative analysis, and we show that ENA trajectories are able to represent information unavailable in aggregate models and facilitate interpretations consistent with qualitative findings. This suggests that this approach to ENA trajectories is an effective tool for representing change in network structure over time.
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Readers already familiar with ENA may notice that this is conceptually similar to constructing 6 different ENA models (one for each activity round): a network is constructed for each student in the data for each round. However, projecting each round in its own ENA space would make it difficult to compare one round to another. Instead, networks for six rounds were included in the same projection, and nodes were positioned so as to interpret the resulting space. SVD is a rigid-body rotation, which means it preserves betweenness, so the interpretation of any single student’s network will be the same in any projection. As with any change in projection, different projections may highlight different statistical properties of networks; however, we did not conduct statistical tests in the trajectory space—although we note that such a modeling approach inherits the constraints and affordances of longitudinal data generally in terms of sample sizes, effect sizes, and issues of covariance when data from individual students are recorded at multiple time points. A detailed discussion of these issues is beyond the scope of this paper, which aims to examine one format for visualizing trajectory data in ENA.
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Acknowledgments
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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Brohinsky, J., Marquart, C., Wang, J., Ruis, A.R., Shaffer, D.W. (2021). Trajectories in 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_8
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