Laura M Schwab-Reese, Nicholas C Lenfestey, Amelia W Hartley, Lynette M Renner, Tyler Prochnow
{"title":"Network Analysis to Visualize Qualitative Results: Example From a Qualitative Content Analysis of The National Child Abuse Hotline.","authors":"Laura M Schwab-Reese, Nicholas C Lenfestey, Amelia W Hartley, Lynette M Renner, Tyler Prochnow","doi":"10.1177/15248399241283144","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47956,"journal":{"name":"Health Promotion Practice","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Promotion Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/15248399241283144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Health Promotion Practice (HPP) publishes authoritative articles devoted to the practical application of health promotion and education. It publishes information of strategic importance to a broad base of professionals engaged in the practice of developing, implementing, and evaluating health promotion and disease prevention programs. The journal"s editorial board is committed to focusing on the applications of health promotion and public health education interventions, programs and best practice strategies in various settings, including but not limited to, community, health care, worksite, educational, and international settings. Additionally, the journal focuses on the development and application of public policy conducive to the promotion of health and prevention of disease.