Reading :: The Coding Manual for Qualitative Researchers

The Coding Manual for Qualitative Researchers
By Johnny Saldana

I can't remember whether I saw this book cited in a recent article or whether Amazon suggested it to me—but I bought it alongside the third edition of Qualitative Data Analysis: A Methods Sourcebook, the classic Miles and Huberman sourcebook that Saldana recently updated. (I'll review that one soon too.)

In any case, this present book is all about coding, a move in qualitative research that involves interpreting, analyzing, and organizing data. "A code in qualitative inquiry is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data" (p.3). It's primarily an interpretive act (p.4).

Coding is often treated in qualitative research texts, but as a chapter or a section, not an entire book. Consequently, we typically get a restricted, crabbed idea of what coding involves. By giving himself the room to really explore coding, Saldana provides a broad synthesis of what others have said about it, and he consequently is able to systematize it and provide many different strategies for it.

In Chapter 1, Saldana describes what coding is, what it involves, and how to use computer-aided qualitative data analysis software to implement it. He explains how coding represents an interpretive, analytical act and prescribes ways to code solo or as a team.

In Chapter 2, he goes on to discuss analytic memos and their role in generating codes and categories of codes.

Chapter 3 is where coding actually begins: Saldana describes first-cycle coding methods, "processes that happen during the initial coding of data" (p.58). By methods, Saldana essentially means discrete categories of coding approaches: grammatical, elemental, affective, literary & language, exploratory, and procedural methods. Each is associated with different types of codes. For instance, affective methods involves emotion coding, values coding, versus coding, and evaluation coding. That is, Saldana has developed an organized taxonomy of coding approaches that might be appropriate for different kinds of studies and research questions. And he gives us explicit guidance in selecting the right coding approach(es). By laying out and taxonomizing these coding approaches, Saldana allows us to see coding in a much more organized and expansive way than we might have when reading more restricted discussions of coding.

Chapter 3 comprises pp.58-186 of the book. It's an exhaustive discussion of each category, making this book more of a sourcebook than a discussion (much like Qualitative Data Analysis: A Methods Sourcebook).

Chapter 4 suggests what to do after first-cycle coding. It suggests performing "eclectic coding," a sort of open coding approach that "employs a select and compatible combination of two or more First Cycle coding methods" (p.188). After eclectic coding comes code mapping: listing the codes, categorizing them meaningfully, constructing major categories as moieties (group divisions), then phrasing the divisions in "versus" terms (pp.196-198). Code landscaping comes next: organizing and examining codes through basic approaches such as word clouds and outlining. Next, Saldana walks us through operational model diagramming, which depict processes in the data, and tabletop categories (i.e., affinity diagrams).

In Chapter 5, Saldana discusses second-cycle coding methods, which allow us to do further, more complex analytical work. These methods focus on Sources, Description, Applications, Example, Analysis, and Notes (p.207).

In the final chapter, Chapter 6, Saldana gives us strategies to pursue after second-cycle coding: focusing, theorizing, formatting, writing, ordering, networking, and mentorship.

If you're getting the idea that this sourcebook is essentially a portfolio or catalog of coding strategies, you're not wrong—and I'll likely turn to this book when I design my next research study. But it's also a meditation on what coding is, what it should do, and how it can furnish a stronger structure for one's analysis. If you conduct qualitative research, I strongly recommend it.