In the context of personal networks, most data is based on respondents reporting on the own relation of their ties ( McCarty and Govindaramanujam, 2005). ties and alters) and in the qualitative interpretation of interaction contexts by the informants themselves. Accordingly, we show that the graphic representation of relationships can be used in an innovative way to collect data from personal networks, both to obtain concrete information about relationships (i.e. In this paper, we explore the contributions of visualizations when collecting personal network data, as well as its use to elicit the qualitative interpretation of individuals about their personal networks. 242), apart from allowing the detection of conflicting goals and areas with potential for cooperation. Thus, through the use of participatory tools to elaborate sociograms, participants make “implicit knowledge about networks of influences explicit” ( Schiffer and Hauck, 2010, p. There are instances where a network visualization is developed during the data collection with the help of the respondents who collaborate and work together through a collective effort. Only recently has the application of visualization during data collection begun to be used ( Carrasco et al., 2006 Hogan et al., 2007 Maya Jariego and Holgado, 2005 McCarty and Govindaramanujam, 2005 McCarty et al., 2007 Schiffer and Hauck, 2010).
Visualizations are commonly used to discover two kinds of patterns: social groups – a group of nodes highly linked to each other – and social positions – a group of nodes who are linked in the social system in similar ways ( Freeman, 2000). Such visualizations tend to be used to observe systematically the relations data and to detect emergent properties that may only be visible through the structure of the network. However, the use of visualizations to depict already collected data has predominated.
Since their inception, visualizations have been integrated in social network analysis in creative ways ( Freeman, 2004 Hogan et al., 2007 Ryan and D’Angelo, 2018).
Jacob Levy Moreno produced the first sociograms in the 1930s and over the years, they have evolved from ad hoc drawings to sophisticated visualizations, largely due to the new possibilities offered by computer and software development ( Freeman, 2000 Moreno, 1934). Graphic representation of relational data is one of the central elements of social network analysis ( Freeman, 2004). For this, the author presents findings from a study of highly skilled migrants living in Spain ( n = 95) through which the author illustrates the challenges, in terms of data reliability, validity and burden on both the researcher and the participants. This allows the author to demonstrate that analytical procedures reveal aspects of the structure of personal networks that respondents are not aware of, as well as the advantages and disadvantages of using both modes of data collection. Finally, the author compares the graphic representation obtained through spontaneous, hand-drawn sociograms with the analytical visualizations elicited through software tools. This allows the author to reflect on the role of social circles in determining the structure of personal networks. Next, the author explores how the visualization of groups in personal networks facilitates the enumeration of the communities in which individuals participate. The most frequent strategies consist in identifying the key individuals, dividing the personal network in groups and classifying alters in concentric circles of relative importance. Second, the author describes the reactions and qualitative interpretation of the interviewees when they are presented with an analytical visualization of their personal network. First, the author shows a procedure by which the visualization is integrated with traditional name generators to facilitate obtaining information and reducing the burden of the interview process. In this paper, the author describe the use of visualization in interview-based data collection procedures designed to obtain personal networks information, exploring four main contributions. The graphic representation of relational data is one of the central elements of social network analysis.