Qualitative analysis is difficult.  We can wish it wasn’t so but the fact remains that the nature of qualitative research, by definition, makes analysis pretty messy.  Unlike the structured borders we build into our quantitative designs that facilitate an orderly analytical process, qualitative research is built on the belief that there are real people beyond those quantitative borders and that rich learning comes from meaningful conversations.

But the course of a meaningful conversation is not a straight line.  The course of conversation is not typically one complete coherent stream of thought followed by an equally well-thought-out rejoinder.  These conversations are not rehearsed to ensure consistent, logical feedback to our research questions; but instead are spontaneous discussions where both interviewee and interviewer are thinking out loud, continually modifying points of view or ideas as human beings do.

The messiness of the interconnections, inconsistencies, and seemingly illogical input we reap in qualitative research demands that we embrace the tangles of our conversations by conducting analyses close to the source.  While this means hours analyzing audio and/or video recordings, it is what is necessary.  It is what we signed up for.

I am reminded almost daily of the challenge qualitative researchers face in analysis.  I see this challenge when I read an article such as this one in Quirk’s devoted to “a structured approach” to qualitative analysis; when a Twitter feed during The Market Research Event alerts me to several speakers espousing “better, faster, cheaper” qualitative research; and from my own studies which have lately involved turning over reams of written transcripts that have been misused and misconstrued by clients who cherry-pick the content.

So qualitative analysis is hard.  We can use all the technology in the world to capture specific words and sentiment but we cannot make qualitative analysis something that it is not.  As Maher et al. (2018) acknowledge, computer coding of qualitative outcomes has its place (e.g., in data management) yet it sidelines the all-important role of the human interaction that takes place in a qualitative research environment.

As in everything we do, researchers want to understand how people think.  And our analytical efforts should acknowledge that people do not think in a straight line.  Maybe it would be useful to take a lesson from Mark Gungor and imagine that our research participants are women whose brains consist of a “big ball of wire” where everything is connected to everything else, in contrast to men whose brains are “made up of little boxes” that are isolated and don’t touch.  Wouldn’t it be nice if analysis was just about opening up a self-contained box, extracting neat thoughts, and moving on to the next box?

Maher, C., Hadfield, M., Hutchings, M., & de Eyto, A. (2018). Ensuring Rigor in Qualitative Data Analysis: A Design Research Approach to Coding Combining NVivo With Traditional Material Methods. International Journal of Qualitative Methods, 17(1), 1–13. https://doi.org/10.1177/1609406918786362

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