Kennedy David P, Green Harold D, McCarty Christopher, Tucker Joan
RAND Corporation.
Field methods. 2011 Aug;23(3):287-206. doi: 10.1177/1525822X11399702.
Network-based interventions are gaining prominence in the treatment of chronic illnesses; however, little is known about what aspects of network structure are easily identified by non-experts when shown network visualizations. This study examines which structural features are recognizable by non-experts. Nineteen non-experts were asked to pile-sort 68 network diagrams. Results were analyzed using multidimensional scaling, discriminant analysis, cluster analysis, and PROFIT analysis. Participants tended to sort networks along the dimensions of isolates and size of largest component, suggesting that interventions aimed at helping individuals understand and change their social environments could benefit from incorporating visualizations of social networks.
基于网络的干预措施在慢性病治疗中日益突出；然而，当向非专业人士展示网络可视化时，对于网络结构的哪些方面容易被他们识别却知之甚少。本研究考察了非专业人士能够识别哪些结构特征。19名非专业人士被要求对68个网络图进行堆排序。使用多维缩放、判别分析、聚类分析和PROFIT分析对结果进行分析。参与者倾向于根据孤立节点和最大组件大小的维度对网络进行排序，这表明旨在帮助个人理解和改变其社会环境的干预措施可能会受益于纳入社交网络的可视化。
