After mastering the fundamentals of theory-driven empirical networks research, there are many options for what to do next. If you do not yet have a particular project in mind, reading widely can be a valuable source of inspiration – hopefully this book has conveyed that the range of possible applications is broad. If you do have one in mind, reading about methods of analysis can help choose a plan appropriate to the project. This chapter is designed to help select a way forward.

Once the data are collected and cleaned, we can start to explore features of the network. Taking an initial look at descriptive network statistics is a good way to take an overview of the data and to spot red flags that signal a problem with the data entry or cleaning. The earlier these can be identified, the better. This chapter serves as a tutorial for using R to do so using the igraph package. It introduces the process of importing a data file into R and walks through the first things you might do with the data, including computing descriptive statistics of the structural features, integrating substantive features of nodes and links, and visualizing the network.

The move from theory to empirics requires figuring out how to collect evidence that could support or disconfirm hypotheses derived from your theory. Empirically studying the network in your theory requires two steps: determining which nodes to include in your data and operationalizing the link type. This chapter helps a reader select the boundary that contains the nodes of interest, pointing out some subtle downsides to random sampling in network studies. It also helps readers determine whether they want to measure full networks or ego ones and offers pointers on operationalizing link types.

This chapter introduces some technical details about networks. Although they may seem like a complication that could be saved for later, the details presented here are actually a useful starting point. They will provide a sense of the many options for ways that a network can matter, which is helpful to have in mind when constructing a theory that will guide data collection. A social network is a record of a set of relationships – links – among actors in a group of interest. Depending on which relationships are present, an individual may find herself in a very different network position than someone else. Different groups can have different patterns of relationships, which means there can also be variation across networks. This chapter will help us be precise in these comparisons across actors and across networks and will highlight why they can be relevant to empirical research.

An empirical social networks study is concerned with what a well-defined social network is like, and whether and how it matters in some context of interest. Designing a successful one requires serious thinking on the front end about what the network is and what it does in theory. This book aims to help researchers do just that. To begin, this chapter motivates this research area with examples from political science, explains why the topic is unique enough to warrant a whole book, and offers guidance on how to know if your research should incorporate networks.

Once the theory is specified and an operationalization has been chosen for the nodes and links, the next step is to acquire the data. This chapter goes deep into issues that arise when designing surveys to collect data. Although this is not the only method of data collection, it is one that illuminates issues that pertain to all others. This chapter covers the practical question of how to use surveys to elicit network information. The advice leans heavily on a well-formulated theory.

Theory is the essential foundation on which an empirical network study is built. A network theory stipulates a certain, carefully defined network and offers a reason why it relates to other variables. Pinning down what the precise network of theoretical interest is and fleshing out a reason why it matters is what makes up the key preliminary work in empirical networks research design. It can be tempting to rush through this preliminary step, especially when data are readily available. Note that doing so comes with risks. Design blunders are more debilitating in networks research than in other data collection endeavors. Thinking through all aspects of a theoretical setup takes time, but is part of the real work of research design. Taking the time early is an investment in avoiding wasted effort later. This chapter presents a framework to help construct a theory that is maximally useful for guiding empirical research design.

Once the information about nodes, links, and their substantive attributes has been collected, a bit more work is needed to prepare to use the data. This chapter covers this intermediate step, with tips for organizing and cleaning the data. Reading this chapter before collecting the data in the first place will help avoid some serious pitfalls. It covers ethical issues pertaining to collecting names (a necessary step in most methods of network elicitation), a method for automating the cleaning of name data, and robustness checks that can be done to assess the cleaning.

Chapter 2 focused on the structural features of networks. These features are determined by the links that are present and how they are arranged among the nodes. Different arrangements of links lead to different shapes, which has consequences for how things might spread through the network, which nodes are important, and how cohesive a collection of nodes is. In empirical research about social networks, the real nodes and links in question will have substantive meaning. Adding those substantive labels to the nodes and links is a start, but we might have additional information about the nodes and links that we would like to incorporate in our study. It is possible to integrate substantive information about nodes and links with structural information in ways that can enrich a network study.