The traditional research approaches common in different disciplines of social sciences centered around one half of the social realm: the actors. The other half are the relations established by these actors and forming the basis of “social.” The social structure shaped by these relations, the position of the actor within this structure, and the impact of this position on the actor are mostly excluded by the traditional research methods. In this chapter, the authors introduce social network analysis and how it complements the other methods.
TopWhy Relational Approach Matters?
The term “social sciences” encompasses a broad spectrum of disciplines ranging from economics and sociology to business management and industrial psychology. Throughout their histories, these disciplines have focused almost exclusively on individuals and their features (Marin and Wellman 2010). Virtually every research method, instrument and mathematical and statistical tool of these disciplines are shaped accordingly.
For instance, let us assume that we would like to find out the contributing factors to the success at university courses through traditional methods. In order to do so, we would start by collecting information about “personal” features of students such as age, sex, place of birth and family income level, and gather all the information in the form of a table. The data in the table are then analyzed with descriptive statistical methods suitable for the study and theories and hypotheses are put forward. The hypotheses are then tested with regression models belonging to the same method category. The philosophical assumption behind this approach is as follows: The whole called “society” can be understood by analyzing all of its actors. Thus, in order to understand the functions of economic classes within the educational system, such an approach would first group students according to their income levels and then compare the average grades of each group (eg Turan and Aktan (2008); Silvester, Anderson, and Patterson (1999)). In this case, the “social” aspect of the phenomenon is reduced to an average. A more “social” study might be conducted with the same traditional methods; for example, a survey among students in order to study the effects of belonging to same or different income groups or those of gender groups on friendships. Once again, the “socialness” is analyzed through the average individual’s point of view. In disciplines like economics, the unit of analysis is either companies or countries instead of individuals, but the basic assumption and methods are not essentially different.
Although this rather traditional approach common to different subdisciplines of social sciences reveal certain explanatory factors, it only looks at one side of the coin: social actors. On the other side of the coin, there are relations established by these actors and forming the “social”. The social structure shaped by these relations, the position of the actor within this structure and the impact of this position on the actor are completely excluded by the traditional method.1
We are living in an increasingly interconnected society. Industrialization, urbanization, informatics and, most recently, developments in modern life under the title of globalization bring about new lifestyles entwined with more frequent and intense daily interactions between social actors. The issue of social networks research emerged in correspondence with these developments long before Facebook and will probably remain present even after it becomes obsolete. Within the framework of this term, many methods relying on computer sciences and mathematics are being developed. Media like Facebook and Twitter, the first items to come to one’s mind upon the mention of social networks, are also included in this research activity. The abundance of data concerning social relations accumulating on such social platforms allows us to use suitable methods, and thus provides a unique opportunity to reveal the way contemporary social life functions. This fast developing field of network research has proven itself useful in a variety of fields: business relations (Artto et al., 2017;Dagnino et al, 2015), policy testing and development (Raberto et al, 2019), learning management (Saqr nad Alamro, 2019), etc. In fact the same base of network analysis tools and metrics are used beyond social sciences suchas biology (Shoshi et al, 2015) and informatics (Nyati et al., 2019). These developments highlights new research opportunies, and business applications, as well as presenting new methodological challenges (Crossley and Edwards, 2016), ethical issues related to use of data (Floridi and Taddeo, 2016), and challenges related to big data (Tinati et al., 2014).