Network Analysis to Visualize Qualitative Results: Example From a Qualitative Content Analysis of The National Child Abuse Hotline.

IF 1.6 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Health Promotion Practice Pub Date : 2024-10-05 DOI:10.1177/15248399241283144
Laura M Schwab-Reese, Nicholas C Lenfestey, Amelia W Hartley, Lynette M Renner, Tyler Prochnow
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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.

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可视化定性结果的网络分析:国家虐待儿童热线》定性内容分析实例。
数据可视化(如通过网络分析创建的图表)可能是呈现来自定性分析的更完整信息的一种方法。定性编码数据的片段可被视为网络分析中的对象,从而创建代码频率(即节点)和共现(即边)的可视化表示。通过分享一个将网络分析应用于定性数据的例子,然后将我们的分析过程与其他应用进行比较,我们的目标是帮助其他研究人员思考这种方法可以如何支持他们对定性数据的解释和可视化。网络分析共包括 265 份求助者与国家虐待儿童热线危机顾问之间的去标识化文字记录。对话后调查,包括求助者对对话的看法,也包括在分析中。我们进行了定性内容分析,并将其量化为记录中每个代码的存在与否。然后,我们根据求助者的看法对数据集进行了划分。回答 "是/可能 "的求助者在谈话后感觉更有希望,他们被归入 "有希望 "数据集,而回答 "否 "的求助者被归入 "无希望 "数据集。这些信息被导入 UCINET 以创建共现矩阵。Gephi 用于将网络可视化。总体而言,"有希望 "对话中的代码共现网络更为密集。此外,这些有希望的对话中的平均程度较高,这表明有更多的代码持续存在。有希望对话中的代码包括信息、顾问支持和问题解决。相反,非希望型对话则侧重于信息。总体而言,网络分析揭示了传统定性分析中不明显的模式。
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来源期刊
Health Promotion Practice
Health Promotion Practice PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
3.80
自引率
5.30%
发文量
126
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.
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