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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.

A project’s aims can often lead researchers to be readily able to identify one (or more) of these tie types as its primary conceptual focus. Furthermore, in many cases, this conceptualization is easily translatable into a measurement strategy. However, in other cases, simply because that identification is conceptually possible does not mean that gathering data on that tie type in the theoretically salient dimension is equally viable. For example, suppose your interest is in mapping the risk-relevant network that promotes a chlamydia epidemic. The relevant network that you would want to map would include all sexual contacts (interactions) that occur between sero-discordant partners.²¹ Additionally, sero-discordance is not a permanent status, so to properly map that risk network, you’d need those interaction data at the level of individual acts, along with each individual’s time-specific sero-status. It is highly implausible that this level of measurement precision would be available to even the most scrutinizing researcher’s data collection efforts.

      ²¹ That is, one partner who has chlamydia and one who does not. In other words, your interest is in data on the population of potentially transmitting interactions.

      While this particular example is extreme, it reflects a common occurrence in social network data collection efforts. There often arises conceptual slippage between the level at which researchers desire to gather data and the level that is accessible to them. That is, research must regularly rely on relational proxies—often that move “up” in the level of generalization (i.e., from flows toward social relations). We may only be able to measure social relationships that include sexual contact, not each sexual act, when studying a chlamydia outbreak. Tie directionality can similarly require measurement proxies. For example, researchers may have access to only one member of a reported relationship, and if that person reports having provided support to their partner, we must take them at their word that the other partner received that support (but see Barrera, 1986). While careful qualifications within analytic interpretations can potentially acknowledge the limitations of such proxies, researchers have increasingly acknowledged that such slippages have implications beyond the measurement level and have argued for thinking about different types of ties as having different theoretical—as well as methodological—implications that researchers must consider (Kitts, 2014).

      Outline of the Book

      From here, the book builds from the above notions of why we might want network data to provide a set of considerations that must be pieced together when developing strategies to gather it. Chapter 2 elaborates the primary strategies for sampling and measuring social networks and describes how these combine into what is known as the boundary specification problem. In Chapter 3, I describe a number of available platforms for implementing social network data collection, with a focus on how these differentially prioritize some of the elements laid out in Chapter 2. Chapter 4 describes several ethical considerations that are unique to the nature of social networks research. Finally, Chapter 5 addresses data quality in social networks research by demonstrating how it is typically assessed, some common patterns of especially high/poor-quality data, and a few strategies for improving the quality and coverage of social network data. This will be followed by a few brief pointers to areas of opportunity for future development in social network data collection.

      For the next two chapters, I will assume that researchers can match the conceptual aims to the methodological strategies of their studies. I will revisit some strategies for coping with these potential limitations in Chapter 5. Given the relational questions that can arise from the perspectives outlined above, the next chapter turns to how scholars can go about obtaining data to address these types of relational questions.

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