Abstract
Collaborative Problem Solving (CPS) is a socio-cognitive process that is interactive, interdependent, and temporal. As individuals interact with each other, information is added to the common ground, or the current state of a group’s shared understanding, which in turn influences individuals’ subsequent responses to the common ground. Therefore, to model CPS processes, especially in a context where the order of events is hypothesized to be meaningful, it is important to account for the ordered aspect. In this study, we present Ordered Network Analysis (ONA), a method that can not only model the ordered aspect of CPS, but also supports visual and statistical comparison of ONA networks. To demonstrate the analytical affordances and interpretable visualizations of ONA, we analyzed the collaborative discourse data of air defense warfare teams. We found that ONA was able to capture the qualitative differences between the control and experimental condition that cannot be captured using unordered models, and also tested that such differences were statistically different.
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Notes
- 1.
MR is a dimensional reduction that can be applied when the units are divided into two discrete groups. The resulting space highlights the differences between groups (if any) by constructing a dimensional reduction that places the means of the groups as close as possible to the x-axis of the space. MR is frequently used in ENA analyses [1].
- 2.
Because each unit is represented by a single, high-dimensional adjacency vector, ONA can use any dimensional reduction technique that can be used with ENA.
- 3.
The mathematical proof that including vectors and their transpose cause degenerate solutions under SVD and other rotations is beyond the scope of this paper; however, we are happy to provide it upon request.
- 4.
The mathematical details of co-registration are beyond the scope of this paper and can be found in the work of Bowman et al. [1].
- 5.
hENA, or Hierarchical Epistemic Network Analysis, is an extension to ENA that enables researchers to model nested effects of multiple grouping variables rather than one grouping variable using means rotation. Detailed description of hENA can be found in [13].
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Acknowledgement
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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Tan, Y., Ruis, A.R., Marquart, C., Cai, Z., Knowles, M.A., Shaffer, D.W. (2023). Ordered Network Analysis. 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_8
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