The Coding Manual for Qualitative Researchers (3rd edition)

Citation

Wicks, D. (2017), "The Coding Manual for Qualitative Researchers (3rd edition)", Qualitative Research in Organizations and Management, Vol. 12 No. 2, pp. 169-170. https://doi.org/10.1108/QROM-08-2016-1408

The Coding Manual for Qualitative Researchers addresses an important aspect of many qualitative research traditions, the process of attaching meaningful attributes (codes) to qualitative data that allows researchers to engage in a range of analytic processes (e.g. pattern detection, categorization and theory building). It is a book intended to “supplement introductory works in the subject” and provide an extensive collection of coding methods from a range of sources for a variety of purposes. It is a book that is probably best positioned to those in somewhere in the middle of the beginner-experienced continuum of qualitative researchers, especially to those looking for examples of different ways to analyze qualitative data.

Saldaña states that this manual “serves primarily as a reference work” rather than a monograph to be read cover to cover. This is a claim important for a prospective reader to understand, and one that I agree with to a certain extent. A good reference work needs to have widely understood content in order for readers to know what to look for, and in this way the primary organizing scheme of the book into chapters on first and second cycle coding methods (and subsequently into a multitude of subcategories) is difficult to understand without a high degree of familiarity with this terminology. The opening chapter does a good job of exemplifying different approaches to coding and clarifying related terminology (e.g. patterns, codifying, categorization and themes) in a way that is helpful to the novice qualitative researcher. Perhaps less helpful in this part of the manual is the quick reference to dozens of specific coding types that are elaborated upon in later chapters and defined in the glossaries contained in the book’s appendices. Despite what for me is too much material covered in only a surface way to start the manual, it is otherwise well organized, through and thoughtful.

Saldaña’s many examples are very helpful, showing how particular data segments can be coded. Where this was particularly helpful was in the otherwise unclear discussion of selecting the appropriate coding method(s) for a particular study to start Chapter 3. That chapter alone describes 33 choices of “first cycle coding methods,” those that happen during the initial stages of data analysis. Arguably it is difficult to provide a concise answer to that question, because quite obviously the decision rests on many factors related to the researcher and the phenomenon researched. It was therefore interesting to see a short example of how an interview excerpt could be coded using descriptive codes (what is being talked about), in vivo codes (derived from the actual language used) and process coding (conceptual actions relayed by participants), each producing different yet equally valid insights about qualitative data.

Another useful aspect of the manual is the discussion of how computer-aided qualitative data analysis software (CAQDAS) can be used, complete with screen shots from many of these programs. The companion website provides a wide range of online resources, particularly to the CAQDAS options available to researchers. I agree with Saldaña’s claim that manual data analysis processes are perfectly fine for small-scale projects, but can be less than efficient or manageable with larger qualitative data sets. I dislike seeing “manual coding” compared with “CAQDAS coding” because it suggests that a computer does the coding. What appears as an artificial distinction between manual and electronic coding largely disappears as examples are given and emphasis is given to the role of the researcher to provide analytic reflection.

Saldaña does a generally good job of balancing the art and science of coding. From early on in the manual, he makes it clear that coding is “primarily an interpretive act,” one that can be done in a variety of equally compelling ways. He effectively discusses the writing of analytic memos (Chapter 2) in a way that I think is helpful and inspirational for researchers, highlighting how good qualitative research is not only about using good/proper methods, but more importantly about good thinking. By providing a categorization of the ways in which qualitative data can be reflected upon, and indeed become part of a cyclical process of data analysis, readers of all types can likely find new and interesting ways to relate to their data that move them beyond simple description of what is being said and the production of a journalistic account of respondents.

The Coding Manual for Qualitative Researchers seems well positioned to a graduate student or researcher who is looking for a synthesis of the many extant approaches to analyzing qualitative data. Experienced researchers would no doubt glean some techniques and terminology from the manual, but likely ones that make marginal refinements to the approaches they already know and/or use. Novice qualitative researchers, on the other hand, will probably find this manual overwhelming and lacking in a thorough discussion of a manageable number of approaches to coding qualitative data and sometimes awkward integration of coding examples. Researchers and students less familiar with analyzing qualitative data would benefit from reading one of the many good books on the topic, for example David Silverman’s Doing Qualitative Research: A Practical Handbook (Sage), Pushkala Prasad’s Crafting Qualitative Research: Working in the Postpositivist Traditions (Routledge) or Jennifer Mason’s Qualitative Researching (Sage). For those in between, however, the range of examples, suggestions for additional readings, companion website and exercises/activities in the appendices should contribute to expanding the horizons of researchers, educators and students in the social sciences.