Stuart indicated that our identities and beliefs shift as we grow, develop, and learn more. “Researchers must recognize their development and evolution as well as examine earlier findings.” He recommends researchers go back to their dissertations and precious research and reevaluate their findings as their ideas and analysis may have changed.  While I wonder how sustainable and difficult that might be, especially if your career as a researcher is a long one. But it also gives us that constructivist perspective that knowledge is fluid and ever-changing.

* And do not drown in a self-created sea of primary-cycle codes.

One of the most compelling aspects of Saldaña’s work is the dynamic, iterative nature of coding. He challenges the assumption that data speaks for itself, emphasizing that coding is a subjective process influenced by the researcher’s positionality and analytical choices.

This raises an essential question for qualitative researchers:
- How do we ensure that our coding process remains both analytically rigorous and ethically responsible, given that our own perspectives shape the way we assign meaning to data?

A powerful takeaway from this chapter is Saldaña’s assertion that coding is never neutral; researchers bring their own biases, experiences, and theoretical frameworks into the process, where "coding is deep reflection, not just labeling—it is about capturing meaning and insight," reminding us that coding is not just about organization but interpretation.

This raises an essential question:
Since coding is inherently shaped by the researcher’s lens, how can we balance subjectivity with analytical rigor to ensure credible and trustworthy qualitative findings?

A key takeaway is that coding is iterative—researchers often revisit and refine their codes multiple times to ensure accuracy and depth of interpretation. Furthermore, reflexivity plays a crucial role, as researchers must acknowledge how their positionality influences the coding process.

Now I wonder, how can researchers ensure that their coding process remains flexible enough to capture emerging insights while maintaining consistency and reliability in their analysis?

o   Bold means X

o   Italics means Y

o   Highlighted in yellow means…

These themes become key pieces in the Findings section of the study.

I resonated with lean coding and hope I can narrow my headings in alignment with my research questions.

• “...lumping large swaths of data into big general categories may not lead to as insightful interpretations as fracturing the data into smaller slices, each with a more specific code” (p. 220)

I always wondered, "Why code so much and not just code for big themes/ideas?" Yet, for the analysis to be comprehensive and rigorous, it makes sense to me now that you would need to find many more independent smaller codes that can be organized or clustered in different ways to create different themes. It also helps you find intersectionality and gives you more data to choose from instead of a big chunks of data. I like the idea of coding being similar to finding the "ingredients" to a meal. Not "Hot Salsa" but "tomatoes, salt, pepper, jalapeños, etc"!

I am in no way intending to diminish the level of Stewart's study, rather, I hope to highlight the importance of the habitual practice of deep reflection. In the words of Charlie McKesy, "We have such a very long way to go [...] but look how far we've come."

Like a researcher, Alice uses prisms as a lens to draw out (analysis) the pieces of Harriet (aka data) and then reassemble them (synthesize). --> Axial coding!

In a parallel, musical interpretation, Simon & Garfunkel sing,

"My eyes can dimly see
The pattern of my life
And the puzzle that is me.

From the moment of my birth
To the instant of my death
There are patterns I must follow
Just as I must breathe each breath
...
Like the color of my skin
Or the day that I grow old
My life is made of patterns
That can scarcely be controlled"

S & G express a lack of control over these patterns, perhaps they would have benefited from learning how to code their observations in order to make sense of them.

And then keeping in mind that data coding is an iterative process, knowing that you'll have to read your transcripts at least 3 times, simplifies the coding and analysis processes.

• What are people doing? What are they trying to accomplish

• How exactly do they do this?

• What specific means and/or strategies do they use?

• How do members talk about characterize and understand what is going on? What assumptions are they making?

• What do I see going on here?

• What did I learn from these notes? Why did I include them?

• How is what is going on here similar to or different from other incidents or events recorded elsewhere in the field notes?

• What is the broader import or significance of this incident or event? What is it a case of?

• What strikes you?

• What surprised me?

• What intrigued me?

• What disturbed me?

C & C mention working backwards from theme to code and it reminded me of a children's book, Silly Sally (Wood, 1994). The story opens with, "Silly Sally went to town, walking backwards, upside-down."

Now, with qual research on my mind, I can't help but notice that Woods is telling the story of Sally, a researcher, performing an ethnographic study of a small town. She documents everything she sees (i.e. a dancing pig) and approaches the data with a unique perspective (backwards, upside-down).

Where Saldaña explains the process of coding, C & C extend and continue the process beyond coding and build a bridge of interpreting the results with the end goal of writing a narrative (whether it be Silly Sally or an article is up to you!)

• First-level codes: Details what is happening (who, what, where, and when)

• Second-level codes: Focused on interpretation of data and identification of patterns or themes.

• Qualitative research does not have a formula that can calculate the data’s meaning. It is an interpretive process that synthesizes and consolidates the data’s meaning.

• I’ve seen data analysis software implementing AI coding features. For example, Atlas.ti has AI Coding Beta powered by OpenAI. I wonder how this will shape the field and how IRB (and other researchers) view these developments.

• Conventional – categories derived directly from data

• Summative – counting and comparisons

• Directed – starts with theory

A researcher makes an interpretation of what the text/data contains in qualitative research. I see qualitative research as a process where you collect data, de-construct the data, and then put the data back together in a new way/representation.

In 2001: identity was described as fragmented; students compartmentalized identity facets based on social context; Race and gender were central to self-understanding; and identity was seen as a fixed state to be achieved.

Whereas in 2005: identity was described as fluid, holistic, and interdependent; students asserted an integrated sense of self and navigated social roles with confidence; spirituality and personal philosophy became central to identity; and identity was framed as a lifelong, evolving process.

Reviewing these key shifts enabled me to see how Stewart's development as a researcher influenced her coding, interpretation, and interview framing. These shifts had a huge impact on the research.

• Saldaña defines a code as a "word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data." Coding is the process of organizing and assigning these codes to data segments. 

• Codes serve as essential tools for data reduction and analysis, helping researchers to identify patterns, categories, and themes within qualitative data. They act as heuristic devices that facilitate understanding and interpretation.

This quote stuck out to me and the importance of staying my organized throughout the process.

I thought it was interesting to see the various examples of how different researchers may code the data depending on the interpretation and beliefs.