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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. 

Quantitative ethnographers across a range of domains study complex collaborative thinking (CCT): the processes by which members of a group or team develop shared understanding by making cognitive connections from the statements and actions of the group. CCT is difficult to model because the actions of group members are interdependent—the activity of any individual is influenced by the actions of other members of the group. Moreover, the actions of group members engaged in some collaborative tasks may need to follow a particular order. However, current techniques can account for either interdependence or order, but not both. In this paper, we present directed epistemic network analysis (dENA), an extension of epistemic network analysis (ENA), as a method that can simultaneously account for the interdependent and ordered aspects of CCT. To illustrate the method, we compare a qualitative analysis of two U.S. Navy commanders working in a simulation to ENA and dENA analyses of their performance. We find that by accounting for interdependence but not order, ENA was not able to model differences between the commanders seen in the qualitative analysis, but by accounting for both interdependence and order, dENA was able to do so.