The Qualitative Analysis Trap (or, Coding Until Blue in the Face)

There is a trap that is easy to fall into when conducting a thematic-style analysis of qualitative data. The trap revolves around coding and, specifically, the idea that after a general familiarization with the in-depth interview or focus group discussion content the researcher pores over the data scrupulously looking for anything deemed worthy of a code. If you think this process is daunting for the seasoned analyst who has categorized and themed many qualitative data sets, consider the newly initiated graduate student who is learning the process for the first time.

Recent dialog on social media suggests that graduate students, in particular, are susceptible to falling into the qualitative analysis trap, i.e., the belief that a well done analysis hinges on developing lots of codes and coding, coding, coding until…well, until the analyst is blue in the face. This is evident by overheard comments such as “I thought I finished coding but every day I am finding new content to code” and “My head is buzzing with all the possible directions for themes.”

Coding of course misses the point. The point of qualitative analysis is not to deconstruct the interview or discussion data into bits and pieces, i.e., codes, but rather to define the research question from participants’ perspectives and derive underlying themes that connect these perspectives and give weight to the researcher’s interpretations and implications associated with the research question under investigation.

To do that, the researcher benefits from an approach where the focus is not as much on coding as it is on “living the data” from each participant’s point of view. With this in mind, the researcher (the interviewer or moderator) begins by taking time after each interview or discussion to record key takeaways and reflections; followed by a complete immersion into each interview or discussion (from the audio/video recording and/or text transcript) to understand the participant’s nuanced and intended meaning. A complete absorption (understanding) of each interview or discussion prior to code development allows the researcher to fully internalize each participant’s relationship to the research question, taking into consideration that: 1) not everything a participant says has equal value (e.g., a “side conversation” between the interviewer and participant on a different topic, an inappropriate use of words that the participant subsequently redefines); 2) participants may contradict themselves or change their mind during the interview/discussion which is clarified with help from the interviewer/moderator to establish the participant’s intended meaning; and 3) the tone or emotion expressed by the participant conveys meaning and is taken into account to aid in the researcher’s understanding.

This big picture sets the stage for code development and the coding of content. But now coding is less about the deconstruction of interview or discussion data and more about ensuring that each participant’s lived experience related to the research question is intact and not lying unconscious in the qualitative analysis trap. Coding is simply a tool. A good thing to remember the next time you begin to feel blue in the face.

Image captured from: http://learningadvancedenglish.blogspot.com/2016/04/until-you-are-blue-in-face.html

2 comments

  1. Margaret, as always insightful and pragmatic. Too often, people approach qualitative analysis with the goal of attaching codes to everything – reasoning, once I’ve coded everything I have a clear path to quantifying every bucket and voila’ a direct translation to meaning. The best advice is to listen to what people are saying/ not saying about the research questions of interest and getting them relaxed and talking about their perceptions and experiences. The researcher must immerse themselves in the interviewees perspectives – there will be many nuances within a single interviewee and between interviewees. Trying to understand the main story being told and likely some strands of sub-stories – which is what you are trying to understand. You might use some of those coded buckets to do some post-weighting of themes (example: 8 of the 12 Chief Medical Officers indicated that ‘theme xyz’ was something that was their biggest pain point). The theme(s) might be the barriers that impact the ability to deliver effective care to patients – which may be a constellation of a number of related clinical challenges. Knowing that a relatively large factor in that constellation is theme xyz for 8 of 12 will help give some context – but does not help you understand the problem(s) enough to recommend some interventions to alleviate the clinical challenge.

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    1. Thanks, Joe, for your thoughtful comment. Yes indeed, learning the stories — and the stories within the stories — is essential. And, as I say, codes and coding are just one tool.

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