Given what we learned last week about the power of weak and strong ties on individuals, it is clear that social networks have the power to influence individuals’ behaviors, emotions, outcomes, etc. In this way, social networks provide us with the opportunity to explore human tendencies, and further understand how human beings exist in and engage with modern society. The social network perspective, according to Keim (2011) is “explains social actions not on the basis of individual attributes but in the context of social relations (Wellman, 1988: 31).” (p. 21). That being said, social network analysis steps beyond understanding modern society as a product of individuals’ traits or circumstances, and considers how the interactions between individuals or the interactions between systems influence such actions.
If however, we are considering interactions between individuals, it is important to consider the perspective from which we are viewing such networks. For example, in egocentric studies only the “network immediately surrounding participants is observed” (Robins, 2015, p.40). This means that our perception of a network is limited to what is immediately surrounding participants, and lacks information about how the network expands beyond these people or how it might be experienced by others. Thus, if we were to shift to the perspectives of a different sample of participants, the network likely would look different. Given this, it is important to consider the bias in SNA just as we would in any other traditional social science design.
So then, how does SNA data differ from traditional social science data? SNA data differs from traditional social science data in that it necessitates two data sets, namely the nodes and the relations between them. For example, on our practicum, we had one data sheet with the characteristics / attribute of the nodes– similar to what we would see with traditional social science data (sheet A). However, this data sheet was paired with a data set that recorded the connections or relations between the nodes (sheet B). Thus, one of the main differences in SNA and traditional social science is the format of the data itself. Further, the main unit of analysis in SNA is the node or the tie as opposed to an attribute or outcome in traditional data analysis. In this way, relational data must be treated differently than our average data set.
Though inherently different, relational data still necessitates descriptive analysis so that similarities and differences between nodes and ties can be considered. Through these descriptions researchers are able to consider the similarities and differences in groups within the network, further explaining how humans engage similarly and differently with modern society. However, diverging from traditional data analyses, one key reason that relational data makes predictive analysis challenging is because of causal ambiguity. Yang, Keller, and Zheng (2017) define causal ambiguity as the fact that we don’t know which data point has caused change in the other. Instead, we know that the two are related non some factor.
Overall, there seem to be some similarities between SNA and traditional social science research. However, SNA provides us with a new language and new techniques to further explore human beings’ interactions, emotions, and daily actions in modern society.
A thought-provoking quote:
- “From a network perspective, one would argue that peers are not influential per se, but can become important if they are engaged in frequent contact, build cohesive networks of high density, transmit information quickly, and produce homogenous evaluations and normative pressures” (Keim, 2011, p. 21).