This chapter introduces the Dirtbagging approach to qualitative social science research. The chapter lays out the main themes and arguments of the book, contrasting the Dirtbagging approach to the traditional approaches, and argues that there is no One Right Way of doing research. It also introduces rock climbing as the major motif for the book and what we can learn from rock climbers.

This chapter tries to define qualitative methods, while discussing some of the difficulties with the most common ways to define them. We begin with a rundown of the typical methods of qualitative data collection but note that qualitative data can also be quantitatively analyzed. We then review a lot of the traditional ideas or even stereotypes about qualitative methods, pointing out that they have been repeatedly challenged lately. Consequently, the easy markers of qualitative methods recited in various texts no longer hold up very well. Finally, we discuss when qualitative methods are appropriate and what type of research they let you do.

This chapter seeks to do four things. First, it describes the role of research questions in the larger research process. Second, and building on that first discussion, this chapter dispels some misconceptions about research questions, especially what counts as a research question and why people disagree about this. Third, the chapter discusses strategies for coming up with a research question. Finally, it identifies some of the secrets about research questions relating to challenges and opportunities that can arise, particularly when you are Dirtbagging about in the field.

This chapter discusses how to anchor your work to the academic literature. Importantly, even though there is a lot of advice out there on how to do this, much of it is unhelpful. So this chapter discusses some of the key ways in which people tend to evaluate research—not so much in its nitty gritty details of research design and analysis, but in terms of whether your entire project is worthwhile. I maintain that you can pretty much make any project valuable, but you have to be able to do certain things to convince people of your project's worth. If you can't do those things, then maybe it's not actually a good project.

This chapter is the first of three chapters on research design. Research design is how you explain or justify your decisions about how to collect and analyze your data. Your explanation may not actually be what guided your decisions (the conventional idea of research design is that it takes place before you collect and analyze your data). But your ability to defend your choices is key to how we evaluate research. This chapter addresses general things about planning and executing your research, such as whether you want to map everything out carefully ahead of time or play it by ear. Keeping these things in mind—not necessarily acting on them immediately but letting them inform your decisions—will lead to a better project.

This chapter reviews the various considerations that go into case selection, which everyone has to do (whether you think you do or not). We start with some strategies for figuring out how to select a case if you are in the design phase and don't know which case(s) to choose. Then we turn the various types of cases we use in social science; each type of case comes with its own justifications for why you might choose this case and not that case. Thinking about these justifications can also remind you about the limits of the type of case you have selected and thus what you can (and can't) claim with your study. The type of case you choose will substantially impact what you can do with your project and what type of relationship your study will have with existing theories.

This chapter examines the issues you need to think about carefully when it comes to your data collection. For starters, we discuss how you decide what data to actually collect. Next, we return to one of the banes of a qualitative scholar's existence: the question of how much data are enough; but rather than worrying about what other people think is the answer to this question, we will answer it on our own terms. Finally, we talk about what you can do to really think through the limitations of your data and how to make your project stronger. Skipping these steps can (justifiably) open you up to criticism. Doing them carefully will protect you against some bad falls.

This chapter discusses the process of collecting data in "the field," which I define broadly to include any place you collect your data. I have adopted this ethnographic language because it provides a broadly useful model, even for those of us doing online or archival research. In this chapter, I review the specific strategies fieldworkers use that I have found useful in my work. Some readers, who have never conducted ethnographies, will recognize these strategies, because the strategies are not unique to ethnographers. Most of the non-ethnographic methods texts I have come across have not said much about the mundane realities of data collection, while this is something at which ethnographers excel.

This chapter discusses the central tools you will need as a qualitative social scientist to analyze your data. While there are certainly more advanced analysis tools, content analysis (open and focused coding) and analytic memos (notes to yourself with varying degrees of analysis) will get you through most projects. Designed and perfected by ethnographers, these tools are once again broadly applicable, whether you are conducting formal interviews, using archival data, or reviewing websites and online documents. They allow you to systematically review your data and keep track of the many insights your mind will be swimming with as you do so.

This chapter discusses the tricks and tools you can use to establish causal claims and, ultimately, to give yourself—and your audience—confidence that you aren't just making shit up. The more of these tricks you use, the more confidence you will have. I think of it like climbers laying down pro—the more nuts and cams you embed on the wall, the less likely it is that you will plummet to the ground if you miss a handhold and fall. One piece of pro might pop out if it's poorly placed or there's some loose rock, but if you have several pieces placed, you're still safe. Likewise, the more of these extra steps you take, the more confidence you can have—and if you are wrong, the more you can be forgiven for believing you had it right.

Qualitative scholars frequently face skepticism about their ability to produce high-quality research—and in sufficient amounts. There are many implicit critiques of qualitative methods vis-á-vis quantitative methods when it comes to things like defining qualitative methods (Chapter 2) or making causal inference (Chapter 10). Underlying these critiques are basic misconceptions—on the part of not only critics but also over-eager qualitative researchers—about qualitative methods' inherent limitations. (Bad qualitative research is, sadly, one contributor to these misconceptions.) So part of learning about qualitative methods requires understanding common critiques of qualitative methods, both so you can be prepared to defend your choice of methods and so you can defend against rote critiques.

This chapter summarizes the Dirtbagging approach to qualitative social science and revisits why having a flexible, inclusive approach to qualitative research is beneficial for everyone.