Nonexperts’ Recognition of Structure in Personal Network Data

Abstract

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 nonexperts when shown network visualizations. This study examines which structural features are recognizable by nonexperts. Nineteen nonexperts 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.