Gathering Social Network Data. jimi adams

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Gathering Social Network Data - jimi adams Quantitative Applications in the Social Sciences

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network in much the same way (Wellman, 1988).16 However, just because different networks can be analyzed with the same approaches doesn’t mean they necessarily should be. Different network types could lead to different applications of the same descriptive concepts; many core network ideas (e.g., centrality or communities) have multiple alternate strategies for their measurement, and it’s often easiest to select between those based on differences between the types of networks being described.17 Moreover, the theoretical mechanisms that provide accounts for different explanatory expectations within networks differ substantially depending on the type of network being examined (Erikson, 2013; Fuhse, 2019, Valente & Pitts, 2017). In either case—and as with any solid social science research—the aims of a network study (whether descriptive, explanatory, or otherwise) must carefully consider what the research aims to address in order to determine what sorts of data will allow them to best examine those questions. Here, we must consider what type(s) of ties the questions are about, how readily researchers can actually capture the types of ties required by their research questions, and whether they will be limited to some sort of proxies for the relationships of actual interest.

      16 My former PhD advisor has even been accused at times of literally seeing everything as a network. At a recent workshop we both contributed to, he was the primary person making that accusation.

      17 For a review of various strategies for conceptualizing and operationalizing the differences between centrality measures, see Borgatti and Everett (2006); for a similar treatment of network communities, see Fortunato (2010) and Porter, Onnela, and Mucha (2009).

      18 The terms relationship and tie are often used more or less interchangeably in the social networks literature. I will attempt to avoid this unnecessary confusion, aiming to use tie as the “catch-all” term and relationship in the specific meaning provided here (see also Erikson, 2013; Kitts, 2014). In leaning on examples from others, I may occasionally slip into the literature norm of also using relationship as the generic term.

      19 See Figure 3 in Borgatti et al. (2009). Their typology also includes a fourth type that I will not address in this book: similarities. These are merely dyadic comparisons of some individual attribute (e.g., same gender). While similarities are dyadic measures, they are not conceptually relational by nature. As such, their measurement and modeling are not captured any better by network approaches than by individually oriented research methods and analytic strategies. Similarities are often useful in the analytic modeling of social networks. However, since measuring similarities do not rely on any uniquely network approaches, I leave you to other research methods texts for optimizing their capture.

      Table 1.2a jpg

      aAdapted from Borgatti et al. (2009).

      20 The “Category” label in Table 1.2 should not be interpreted as indicating those differences only apply to the row specified (e.g., interactions can be subjective or objective, and relations can be mutual or directed), but these are primary delineations on the types of ties that are often the focus of research in these domains (e.g., the perception vs. reality of diffusion [knowledge vs. information]).

      Social interactions capture the joint participation by pairs of nodes in shared activities. The types of interactions that are most commonly studied are things like sent and received messages, engaging in sexual intercourse, the joint use of injecting drug equipment (e.g., needles), or other shared experiences (e.g., meals). Interactions are often more temporally fleeting than social relationships and frequently aim to capture the behavioral nature of shared activities—as opposed to the social nature of roles.

      Moreover, the interaction examples provided in Table 1.2 introduce the notion that ties can also be undirected (mutual) or directed. An undirected social relationship looks the same from the perspective of each party involved; each sibling is sibling to the other. Contrastingly, a directed relationship necessarily involves two members of differing, complementary, roles. A parent–child relationship involves two members occupying different roles. Many interactions are directed as well, involving sender and receiver roles (e.g., a speaker and a listener if the interaction is a specific speech unit within a conversation).

      Often these roles or interactions can form the basis for potential flows between partners, which are the final type of ties identified by Borgatti et al. (2009). So, the needle sharing mentioned above may lead to disease transmission, or conversations may allow knowledge to pass from one individual to another. Flows may also be the primary tie type of interest, independent of how roles or interactions shape their possibilities (e.g., in studies of financial remittances). Importantly, scholarship has shown that identifying the actual transmission of ideas through a population (e.g., diffusion of knowledge) can provide considerably different estimates than when we ask people to account for who influenced them on a particular idea (i.e., perception of information flows) (J. Young & Rees, 2013). The objective–subjective distinction here is therefore primarily one for researchers to carefully consider in deciding which is the aim(s) for their research.

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