4 through 6 take up critical topics that need to be considered when gathering social network data. Ethical issues are the subject of Chapter 4. Generally speaking, these issues are the same as with any social research—voluntary participation, consent, and minimization of risks—but there are some peculiarities to social network data, especially the important role of “alters” (are they human subjects?) and heightened risks of deductive disclosure (based on potentially recognizable patterns of connection). Chapter 5 provides a valuable discussion of data quality, including what has been reported about the strengths and weaknesses of social network data in the research literature. Missing data may arise for a variety of reasons, for example, each of which has different implications for inferences analysts might wish to draw. Chapter 6 points the way forward, identifying fruitful topics that are likely to be the subject of additional exploration in the coming years.
Gathering Social Network Data is beautifully organized and written. The text is engaging: The voice of the author is present on every page. The volume is replete with examples that illustrate the various choices that researchers must make in designing social network data. Reviewers were very pleased to see sample instruments in the appendices. This was a first—to have appendices singled out for praise! Whether you are new to the field or an experienced practitioner, you will appreciate this volume.
—Barbara Entwisle
Series Editor
Preface
Methods books generally take on one of two flavors. One option is basically a book of recipes. “You want to identify the effect of parental education on children’s mortality? Here’s the model and measures you need, how to gather them, what pitfalls to avoid, etc.” Another approach is basically to illustrate a set of examples of how people have done something in the past, then use those examples to derive a set of principles that scholars ought to follow. If the first is a recipe book, the second is a book on the principles of cooking. “Here’s how I selected which observations to record vs. which to ignore, how soon after fieldwork I made sure to translate my observations into fieldnotes, and the details of how I coded up those observations, including the version of the software I used.” From these practices, authors teaching ethnographic methods would extract general recommendations of best practices for standards of evidence, strategies for recording observations, schema for coding data, and what color pens you need to use to record those observations. OK, maybe they won’t get hung up on pen color, but you get the idea.
Here, I’m going to take the approach of focusing on the principles (rather than recipes) necessary for gathering social network data. That is, I’m rarely going to provide a single set of recommended practices that apply in all scenarios. Instead, I’m going to draw on examples of what people have done in previous studies to illustrate the various trade-offs that stem from different approaches to addressing the key sets of principles introduced across the chapters here. Given both (1) the variety of theoretical perspectives represented in the field and (2) that social networks is still a relatively young field in some aspects of its development, it should come as no surprise that there will be a variety of approaches—even for how best to address the same principles—represented within network scholarship.
Hopefully, this book will provide the tools allowing you to assess why that variety is appropriate (and occasionally when it is not) and then to draw on those principles and their applications in the examples used to help you design your own social network data collection efforts.
A Brief Note on Reading This Book
Given this variety, there remain quite a few unsettled debates on how social networks research is practiced. In the text, I focus on the primary principles that must be considered in any social network data collection project. Additionally, I also present many of the most common approaches to the resolution of these in practice. Further, I rely on a relatively liberal use of footnotes, often to provide pointers to alternatives to those common approaches. If you’re new to the field, it may be best to read without the footnotes to avoid getting into the details of what may at this point seem like internecine debates in the particulars of methodological approaches. However, if you’re looking to take a deeper dive into some of the (conflicting perspectives and historical roots of) material presented or make particular decisions in the design of your study, the footnotes frame many of these debates and provide pointers to other resources intended to facilitate that further exploration.
I also want to mention here at the outset one key aspect that I will not really address in this book. This is a book on the methods of data collection, not on social network analysis (Knoke & Yang, 2007). There are literally dozens of good books available on social network analysis (SNA), and I don’t feel the need to compete with them or to replicate their work. If analytic approaches to network data are what you’re after, I’d point you to one of those books (for a few excellent examples, see Appendix A).1 The aims of this book are complementary to these others. In fact, I make a few assumptions in this book that you are familiar with many basic terms in social network analysis, to avoid unnecessarily breaking up this text with those definitions. However, for those of you who are new to the field or just want to be sure we’re working from the same assumptions,2 I provide a glossary in Appendix A that provides definitions for many key terms (appearing in bold where they first occur in the main text).
1 Currently, most treatments of gathering network data are single chapters in larger books, which in turn correspond with the single units in large SNA courses. Most people come to realize the need for gathering social network data after having some sense of the theoretical or analytic aims they have for that data. As such, I expect for most readers that this book will not be your introduction to the field of social networks, but you will come to it with some familiarity with SNA’s basics.
2 After all, this is a highly interdisciplinary field, and depending on which tradition you’re coming from, these various perspectives occasionally have multiple words that mean the same thing or use the same word to indicate different ideas.
Acknowledgments
I’ve often wanted a book focused on network data collection while teaching this material in courses and workshops over the past decade. So I’m grateful for Barbara Entwisle and Helen Salmon providing me the opportunity and guidance to write one for this series and for overseeing a review process that undoubtedly improved the book. Hopefully, others will find what’s here at least a fraction as useful as I’ve found the process of writing it.
Any project benefits from others’ feedback, and this book is no different. Throughout, I can see the fingerprints of several collaborators from over the years. Many of the ways I approach networks date to Jim Moody’s influence as my PhD advisor, when working with him provided my introduction to social networks research. In the years since, Ryan Light and David Schaefer have been regular sounding boards on basically all things networks. It’s hard to tell where much of my own perspective begins and theirs end at times. Except when it comes to any lingering misunderstandings or misrepresentation of ideas from the field; I manage those all on my own.
The organization of this material mostly stems from teaching opportunities I’ve had across a range of settings: courses at Columbia University’s Epidemiology and Population Health Summer Institute, American University, University of Colorado Denver, and the Inter-university Consortium for Political and Social Research’s
