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Coding data—defining concepts and identifying where they occur in data—is a critical aspect of qualitative data analysis, and especially so in quantitative ethnography. Coding is a central process for creating meaning from data, and while much has been written about coding methods and theory, relatively little has been written about what constitutes best practices for fair and valid coding, what justifies those practices, and how to implement them. In this paper, our goal is not to address these issues comprehensively, but to provide guidelines for good coding practice and to highlight some of the issues and key questions that quantitative ethnographers and other researchers should consider when coding data. 

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. 

A key goal of quantitative ethnographic (QE) models, and statistical models more generally, is to produce the most parsimonious model that adequately explains or predicts the phenomenon of interest. In epistemic network analysis (ENA), for example, this entails constructing network models with the fewest number of codes whose interaction structure provides sufficient explanatory power in a given context. Unlike most statistical models, however, modification of ENA models can affect not only the statistical properties but also the interpretive alignment between quantitative features and qualitative meaning that is a central goal in QE analyses. In this study, we propose a novel method, Parsimonious Removal with Interpretive Alignment, for systematically identifying more parsimonious ENA models that are likely to maintain interpretive alignment with an existing model. To test the efficacy of the method, we implemented it on a well-studied dataset for which there is a published, validated ENA model, and we show that the method successfully identifies reduced models likely to maintain explanatory power and interpretive alignment. 

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.