Gathering Social Network Data. jimi adams
Oaks : SAGE Publications, [2019] | Includes bibliographical references and index.
Identifiers: LCCN 2019007277 | ISBN 9781544321462 (pbk. : alk. paper)
Subjects: LCSH: Social networks.
Classification: LCC HM741 .A33 2019 | DDC 302.3-dc23 LC record available at https://lccn.loc.gov/2019007277
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Series Editor’s Introduction
It is with great pleasure that I introduce Gathering Social Network Data, by jimi adams. This new volume fills a gap in the QASS series as well as in the literature more generally. There are several excellent texts describing social network analysis, including the one by David Knoke and Song Yang (No. 154) in this series. However, there is no text discussing methods for designing, collecting, and evaluating the data that are the subject of these analytic techniques. Such a text is urgently needed given the impressive growth in social networks research.
An expert on social networks, adams drew on his extensive experience teaching these methods to design a QASS volume that appeals to readers at all levels. For novices, adams introduces topics in a time-tested and systematic way, is careful to define terms (both in the text and in a glossary), and illustrates his points with lots of examples. The volume could serve as a useful supplement in an advanced undergraduate course or graduate seminar. For experts, he explores technicalities and points to current debates in the literature through extensive footnoting. Researchers already well established in the field will also benefit from its cogent discussion of issues surrounding the design, collection, and assessment of social network data.
As adams explains in the preface, the volume focuses on principles, with the goal of providing readers the tools needed to develop their own approach to gathering social network data. Chapter 1 introduces basic concepts, describes theoretical perspectives, and reviews types of ties and the kinds of questions of interest in social research. Chapter 2 focuses on three considerations that must be addressed in any network study: tie measurement (name generators, name interpreters, resource generators), sampling design (ego based, complete, and partial network designs), and inclusion and exclusion criteria (i.e., boundary specification). The ordering of the topics in this chapter is reflective of adams’s own teaching strategy and what has worked best over years of experience. Chapter 3 addresses modes of social network data collection, distinguishing between active and passive approaches. Chapters 1 through 3 are the core of the volume.
Chapters 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