Qualitative Methods and Visualizations in the Study of Friendship Networks

Abstract

One of the most powerful aspects of social network data is the fact that they can reproduce social relationships in a formal and comparable way. Relational matrices abstract from the hustle and bustle of everyday interaction, and systematise information in terms of presence or absence of ties expressing them in a directed or undirected, binary or valued form. While the formal approach represents an advantage of social network analysis, as it allows bracketing off the idiosyncratic and subjective content of social structures, the mathematization of the complex nature of social relationships has also been criticised for the lack of engagement with the subjective meaning and context of relationships. Such stream of critique has called for an increase of use of qualitative methods in social network research. The first goal of the paper is to address these critiques by rebalancing the argument and showing how social network analysis has always engaged with both formal and contextual aspects of social structures. The paper reviews some theoretical perspectives that discuss and systematise a mixed method approach, and explores the methodological advantages of using network visualizations together with qualitative interviews in the collection, analysis and interpretation of personal networks. The advantages of adopting a mixed method approach are illustrated over some examples of friendship networks of 23 single male and female people collected in Milan, Italy, in 2005. A classic name generator is used to reconstruct their egonets of friends, and the visualization is adopted as the input for in-depth interviews with specific attention devoted to the meaning of friendship relationships, the kind of resources they offer, the conflicts and constrains they entail, and how they have developed and evolved over time. By comparing information obtained respectively with name generators and in-depth interviews, the paper shows how the mix of data improves and specify the understanding of personal networks.