This (from R) is not the type of coding this post is about, although of course this does show some code. I was reading Analyzing Qualitative Date (Gibbs, 2007, Sage publications) about a week ago, looking for a reference on open coding. According to Gibbs, open coding is "the opposite of starting with a given list of codes" (p. 45). Beyond, or maybe within open coding is the idea of "constant comparison," that I associate with Glaser and Strauss's grounded theory (The Discovery of, 1967, Aldine) in which data analysts constantly compare excerpts (or words, or in the original GT, lines) to see what might fit with what else. My first cycle open coding, on the other hand, tends to be the opposite of constant comparison. I have noticed that constant comparison is very, very popular among published qualitative researchers, even when GT was not used to frame the research. My guess is because: a) it sounds good, and; b) it provides a systematic way to approach coding.
My fear - even when I start to cluster and categorize data as a prelude to more abstraction - is that I am going to try to force fit things once I have identified or created those places (be they categories or codes) where things can go. So as a result, I have held onto a very 'close to the data' approach to first cycle coding that is more like summarizing the excerpt. I even considered whether I should call this 'summary coding,' - which as far as I know is not a scheme that has been defined or described in the past. As I considered these two approaches further - comparing and not - I started to think about sculpture. Some sculptors work with subtraction - finding things in blocks of marble or tree trunks, while others work with addition - molding, shaping and adding clay or other moldable substances (Play doh?) as needed. This additive method, I think, is a little bit like constant comparison - only you have to imagine multiple sculptures that the researcher places the best fitting pieces of 'clay' on. Whereas I like to think that I am whittling (or carving) away at the corpus of data - beginning with some broad outlines and gradually filling in the detail of the essence that is not immediately obvious but emerges after a careful, cautious process of extraction and shaping.
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AuthorI am Sheryl L. Chatfield, Ph.D, C.T.R.S. I am a member of the faculty in the College of Public Health at Kent State University. I also Co-coordinate the Graduate Certificate in Qualitative Research and I am a member of the Design Innovation Team at Kent State. Archives
February 2024
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