Data visualization, such as figures created through network analysis, may be one way to present more complete information from qualitative analysis. Segments of qualitatively coded data can be treated as objects in network analysis, thus creating visual representations of the code frequency (i.e., nodes) and the co-occurrence (i.e., edges). By sharing an example of network analysis applied to qualitative data, and then comparing our process with other applications, our goal is to help other researchers reflect on how this approach may support their interpretation and visualization of qualitative data. A total of 265 de-identified transcripts between help-seekers and National Child Abuse Hotline crisis counselors were included in the network analysis. Post-conversation surveys, including help-seekers' perceptions of the conversations, were also included in the analysis. Qualitative content analysis was conducted, which was quantified as the presence or absence of each code within a transcript. Then, we divided the dataset based on help-seekers' perceptions. Individuals who responded that they "Yes/Maybe" felt more hopeful after the conversation were in the "hopeful" dataset, while those who answered "No" were in the "unhopeful" dataset. This information was imported to UCINET to create co-occurrence matrices. Gephi was used to visualize the network. Overall, code co-occurrence networks in hopeful conversations were denser. Furthermore, the average degree was higher in these hopeful conversations, suggesting more codes were consistently present. Codes in hopeful conversations included information, counselor support, and problem-solving. Conversely, non-hopeful conversations focused on information. Overall, network analysis revealed patterns that were not evident through traditional qualitative analysis.
Keywords: child abuse; content analysis; network analysis; qualitative; visualization.