This semester I am teaching a qualitative data analysis course with a focus on use of software or QDAS (Qualitative Data Analysis Software). One of the things I am particularly enjoying about this is that I am able to focus on the mechanics and style sof coding - by default most QDAS analysis is coding - this is what these programs were mostly set up to do and remains what they are most comfortable doing. I am not quite 1/3 of the way through the class/semester and I realized that I only barely touched on the idea of 'what should be coded?' This question came to me as I was reviewing a tutorial for HyperRESEARCH, a program I used in the past and think is among the particularly good alternatives for student researchers who are considering purchase of a user license for something. I also like the transcription software, HyperTRANSCRIBE, and know that at least one of my former students purchased this. HyperRESEARCH makes a free trial version available and has some nice coding examples. The amount of coding shown in the examples seemed just about right to me - but I wondered why I though so. So, what should be coded? How much coding is enough coding? I am going to consider this in terms of what I think of as the standard data type for qualitative research - a typed interview transcript, presented in a text or word processing file. By the way, this discussion is somewhat less relevant to classic grounded theory or any other researcher goals that incorporate a lot of reliance on the data to help define as well as refine or address the purpose. Ron Chenail, my favorite (overall/generalist) qualitative research expert, suggested writing your research question or purpose on a sticky note and attaching it to your computer monitor - or wherever you will see it while you code. (For those people who chop up transcripts with scissors and spread them on the floor, perhaps you need a dangling pennant or flag with your questions/purpose.) I think this is great advice and have certainly repeated it elsewhere in this blog, and dozens of times to students. However, sometimes the purpose is pretty big: 'how do X described their experience with Y?,' or pretty vague: 'what are the characteristics of x?' (this was one of my prior studies), or emergent, 'what is the process of xyz?' or not all that directed: 'what was it like living during Y?' Arguably just about everything in a transcript - which is just about everything someone said in response to questions about the purpose - could at least tangentially relate, or might relate to something else that is directly relevant, or might begin to describe a relevant experience, etc.
This - potential to tie in much information eventually - is one argument for pretty dense and open coding, at least in the early stages. On the other hand, I think there is always some minutia that is a stretch to relate - when considered as a free standing excerpt. So taking into account the idea of purpose-driving coding, and one goal of coding I firmly believe in, even with very dense coding, namely data reduction, I offer the following considerations to help guide researchers as they make coding descriptions.
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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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