Episode 4: The Art and Science of Coding in Qualitative Research
In this episode we dive into the complexities and wonders of coding in qualitative research. We’re going to touch on some really interesting perspectives from academia, and even delve into some pop culture references.
EPISODE TRANSCRIPT
Well, hello and welcome to another episode of the Social 730 Lecture Series podcast. Today I have a jam packed episode for you, diving into the complexities and wonders of coding and qualitative research. We're going to touch on some really interesting perspectives from academia and even dive into some pop culture references. So Let's get going!
I Hope you're all doing well and enjoying the various elements of your interviewing assignment. There's so much rich richness to this assignment and I'm really looking forward to learning about all your “A-HA” moments and seeing what emerges for each of you through this assignment.
I also hope that you're now connected with the Nvivo software. I'm really excited to have this set up for all of you as an option. And please remember you, you only need to use the software if it works for you. It's just another tool to add to your researcher tool-box. And this assignments offers you a great opportunity to play with the software and get acquainted with it. Try it out in in a way that you'd actually use it. So have fun with it!
OK, let's dive into coding and data analysis. Linneberg and Korsgaard characterized coding as a craft that enables deep immersion in your data and transparency in the development and presentation of findings. They suggest that codes in their most basic form are simple phrases or words used to label and represent your data.
Similarly, Elliot discusses how coding is a decision making process where the decisions must be made in the context of a particular piece of research. The decisions she's referring to are decisions on how to best represent these data and make sense of them in relation to your research questions. Coding can help us to get to grips with our data, to understand it, to spend time with it, and ultimately to render it into something we can report.
One student in this class once characterized coding as a work of art that we all bring slightly different interpretations to: trying to understand the masterpiece—or if it's a masterpiece! I approach coding as an opportunity to be playful and inquisitive, to be open to the wonder of emergence and interconnection.
We code so much in our daily life. These podcasts, for example, are coding exercises where I look for emergent themes from our learning community and try to connect these into our our weekly episodes. For me, it's a, it's a messy process that never feels fully complete. If we consider coding as a process of making connections with and between ideas or variables and deriving meaning from these connections or nodes, it's possible to begin to demystify the experience of coding and transform the process into an exciting opportunity of discovery.
One key idea around coding that I hope you can appreciate from our unit resources is that there are many ways and approaches to code qualitative data. Each one of these unit resources presents the process of coding in a slightly different way. I did this intentionally; not to confuse you, but to highlight the multiplicity of possibilities that are open when we're coding qualitative data. With quantitative research, there is comparatively little room for researcher decision making once the design has been determined. However, this doesn't mean that a quantitative study cannot integrate some qualitative aspects into its study design.
We're learning in this class that there is a multiplicity of qualitative research approaches. The knowledge you generate through your research—your data— will emerge in different ways due to the dynamics between you—the researcher— your participants, the research questions, and the context in which the knowledge is generated. But keep in mind that it's possible to interpret similar issues or data using different languages and different methodologies.
This is where it gets interesting, because if we take the same data sets and have different researchers with different experiences, different disciplinary backgrounds, different epistemologies using different methodological approaches to analysis, will the results be similar? Does it matter? Isn't this the beauty of Inter or transdisciplinarity? OK, so some things to consider for sure as we move ahead with our reflections on on coding.
Linneberg and Korsgaard provide a nice outline of two distinct ways in which codes are generally developed:
1. First, inductively emerging from the data post collection.
2. And 2nd deductively established arising from literature of or applied knowledge prior to data.
So inductively or deductively. I agree with him that the most typical approach is a combination of inductive and deductive. They call this abductive coding where you cycle back and forth between data, induction, and theory, deduction. Being more aligned to the critical post modernist paradigm, I typically fall into the inductive coding camp, although I still search the literature and develop my interview guides with some ideas or codes in the form of research questions or goals that inform the early phases of the research design.
Linneberg and Korsgaard suggest a 2-cycle approach to coding, where you begin with broad descriptive categories or what they call first cycle and then refine those categories by looking for patterns and categorizations ,or what we would call second cycle. The second cycle categories can also be referred to as themes, or nodes, and this is where you'll sometimes hear about the idea of thematic coding as a form of qualitative analysis, or the use of nodes within qualitative software programs.
Eliot helps to clarify this terminology, noting that themes, nodes, and categories are typically broad ideas that consist of several codes from your data. So, similar Linneberg and Korsgaard, Elliot helps us to better understand that we first develop codes from our data and then based on those codes—and not our data—we begin to organize our ideas into categories or theme.
From my experience, I would say that this is generally correct, but sometimes depending on how much data you have and what kind of methodology you're using, you may go to a third or fourth cycle to really refine the data, categories and themes to find solid patterns. This kind of coding should also be distinguished from analysis.
But I think it's possible to integrate your analysis and coding together, burrowing some lingo from grounded theory. Lindbergh and course guard called this “analytic memoing”— creatively developed little documents based on intuition, hunches and serendipitous occurrence. These little documents can actually be great moments of discovery in your research and provide breakthroughs that will guide your research.
Being mindful of how your biases may impact your coding approach is also important. But we need to embrace the possibility that our professional experiences and cultural epistemologies can provide important frameworks for coding and analysis. It's some methodologies, there are certain processes and language to support the researcher in addressing this idea of bias. In phenomenology, for example, it's called bracketing. And with constructivist grounded theory it's called memoing.
In other methodologies however, our biases are our strengths.
In participatory action research typically, the research has identified an issue that needs action. There's no questioning around how or why the researcher identified the issue. The focus is on developing strategies and or outcomes with your participants to address the issue.
And with indigenous research methodologies, there may be teachings, knowledge frameworks, cultural protocols and practices that all provide important lenses for being able to represent your research in the best possible way.
So do we try to separate or bracket from our biases or do we embrace and reframe them as strengths? And who determines bias and what is the objective threshold?
Have you seen the Netflix documentary “The social dilemma”? It's worth considering how, on a broad scale, our understanding of society, social issues and approaches to challenges might be curated by technology and the regulations of these technologies. How does cancel culture impact our ability to debate challenging ideas, to share new emergent ideas that go against the herd mentality? What really is fake news and how do you critically access data if you can't access the dissenting opinions because they're labeled as fake news? Well, I highly encourage you to watch the documentary and reflect on some of the material being presented in this unit.
Lastly, I included the video resources in this unit to highlight how the Nvivo software can be used to support your coding processes. You can see that the software can really help you organize your research resources, code these resources, and then categorize or theme your codes. Linneberg and Korsgaard and Elliott all suggests that the use of software can allow for a rich coding and analysis experience. However, they caution that using software can also result in the creation of too many codes and not provide researchers with an opportunity to deeply reflect and refocus their data.
Regardless of whether you use software or not, your subject expertise, creativity and intellectual engagement with your data will still inform the coding process.
Well, next week we begin our dive into our explorations of the various methodological approaches to qualitative research with an introduction to grounded theory. Keep working on your assignment twos. And remember, I'm just an e-mail or phone call away. And don't hesitate to reach out anytime!
Thank you all for tuning into Episode 4 of the SOSC 730 Lecture series podcast. I hope you found this episode insightful and informative. If you have any questions, comments or feedback, please feel free to reach out. Until next time: this is Siomonn signing off.
