# data[].attributes.body
Coding is like generating the "bones" of analysis, which are then assembled into a "skeleton".

# data[].attributes.body
Analysis logistics: colors, cutting, or computers? Tracy writes that manual methods/physically interacting with data (transcripts, field notes, photos, etc) = Potentially messy; Good for new researchers or hands-on approach --- I prefer a hands on approach, but not necessarily a messy one...thinking about ways to eliminate said "messiness".

# data[].attributes.body
Whether you choose to code or not depends on your individual value, attitude, and belief systems about qualitative inquiry (Saldaña, 2021).

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

# data[].attributes.body
Without a well-organized data set, analyzing and writing will feel about as inviting as trekking through an overgrown jungle (Tracy, 2020).

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

# data[].attributes.headline
The Coding Process:                          Data  ---> Theory ??

# data[].attributes.subject
The Coding Process:                          Data  ---> Theory ??

# data[].attributes.body
Saldana (2016) addressed the following common critiques against coding: Coding removes context and depth; coding limits interpretive freedom; and coding is outdated/poststructuralists prefer holistic interpretive approaches. If the image represents one approach to how researchers develop theory, what would be different for researchers who approach qualitative methods with a holistic interpretive lens?

# data[].attributes.body
The authors differentiate between various types of codes, such as descriptive codes (summarizing the primary topic of the data), interpretive codes (inferring meaning), and pattern codes (identifying emergent themes or explanations).

# data[].attributes.body
Saldaña discusses the role of Computer-Assisted Qualitative Data Analysis Software (CAQDAS) in facilitating the coding process, highlighting how these tools can assist in managing and analyzing large datasets.

The chapter underscores the significance of writing analytic memos to document reflections, insights, and decisions made during the coding process, aiding in the development of deeper analytical interpretations.

# data[].attributes.body
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.

# data[].attributes.body
I reviewed the language used to describe identity; Negotiating Identity; the Core of Identity; and how Identity Development was conceptualized in 2001 and 2005.

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.

# data[].attributes.body
In "Researcher as Instrument: Understanding 'Shifting' Findings in Constructivist Research," D-L Stewart (2010) presents several key points:

In constructivist research, the researcher plays a central role as the primary instrument of data collection and analysis, influencing the study through personal perspectives and experiences.

Graduate programs should emphasize the development of reflexive practices, helping emerging researchers understand and articulate their influence on research findings.

Acknowledging the researcher's role and practicing reflexivity can enhance the credibility and trustworthiness of constructivist research by making the research process more transparent.

# data[].attributes.body
While Tracy’s phronetic iterative approach offers a reflective framework for qualitative data analysis, one potential critique is that the iterative nature of the process can be time-consuming and ambiguous for novice researchers

The lack of a clear-cut, step-by-step structure may lead to challenges in determining when enough iteration has been done, potentially causing analysis paralysis or overinterpretation of data

How can I balance structured coding techniques with iterative flexibility to ensure both rigor and creativity in my qualitative data analysis?

# data[].attributes.body
In the first study, the researcher's additive questions assumed that identity facets were independent/unrelated. This offered less room for students to share intersectional perspectives. In addition, the framing of the questions resembled the researcher's previous assumptions it and influenced how the data was analyzed.

# data[].attributes.body
3 types of data analysis

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.

# data[].attributes.body
Qualitative data analysis software serves as a tool that efficiently stores, organizes, manages, and reconfigures the data.

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.

# data[].attributes.body
“Quantitative analysis calculates the mean. Qualitative analysis calculates meaning” (pg. 13).

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.

# data[].attributes.body
Primary-cycle coding involves assigning words or phrases that capture the essence of the data while secondary-cycle coding involves explaining, theorizing, and synthesizing.

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.

# data[].attributes.body
I'm in some kinda mood today lol.

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!)

# data[].attributes.body
There is not one way to code, and I'm so glad that these readings show that. However, there is also more ways to interpret, analyze, and write your coding. Will there be readings about how to write our the coding process and analysis/discussion sections of the research paper?

# data[].attributes.body
Reflexivity, introspection, and awareness of how we, as researchers, interpret data, collect data, think about data, and understand data is part of the qualitative research process.

# data[].attributes.headline
Emerson’s & Saldaña’s & Sunstein & Chiserei-Stater’s cumulative list of Qs

# data[].attributes.subject
Emerson’s & Saldaña’s & Sunstein & Chiserei-Stater’s cumulative list of Qs

# data[].attributes.body
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?

# data[].attributes.body
Not quite sure what the purpose or goal of a theme passage is. Is it to help summarize and create the narrative of your findings?

# data[].attributes.body
All lot of these readings are providing too much detail about the coding process that it can be overwhelming! So I love focusing on Tracy's three core questions: what is the data telling me? What do I want to know? What is the relationship between the two?

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.

# data[].attributes.body
Saldaña touches on the role of patterns in coding. And, OK, this is a bit dramatic, but it got this song stuck in my head. The lyrics actually pertain to Saldaña's definition very well. Saldaña writes, "[Patterns are] stable indicators of humans’ ways of living and working to render the world ‘more comprehensible, predictable, and tractable.’"

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.

# data[].attributes.body
Maybe I'm too narrow minded in my approach to qualitative analysis, or maybe I'm not seeing the point of this reading, but when reading through this article, I noticed that there are far more reasons (geographical location, institutional, educational background, cultural and social norms, etc.) that can also affect the findings of conducting a study twice. In fact, it wasn't the same study twice. It was two slightly different studies with the same research question. Even the author indicated that she changed how she approached questions, clarified questions when participants asked, and her interview protocol completely changed. Had the researcher kept everything consistent with the first study in her second study, how would researcher as instrument influenced the results of the second study? How would that have changed?

# data[].attributes.body
Tracy's definition of axial coding stated that is is reassembling data that fractured during open coding. This poked at a particularly nerdy corner of my brain and reminded me of a subplot in The Magicians TV series (season 4, episodes 7-9). Harriet, a rebellious librarian, becomes stuck in a mirror that breaks. Because the mirror breaks, Harriet herself breaks into multiple versions of herself, each an embodiment of some aspect of her character. Alice, a student magician, attempts to reassemble Harriet using prisms.

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!

# data[].attributes.body
Stewart's study is basically a formalized and published version of the self-evaluation that teachers often ask of K12 students - and college students. It is not an uncommon practice for teachers to collect student work samples at the beginning of the semester or year and then have them compare their own work to later samples at the end of the semester or year. In doing so, the goal is primarily to observe growth but also to see how one's perspectives may have changed over time.

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."

# data[].attributes.body
1. Qualitative analysis is an iterative process.

2. Phronetic iterative analysis balances participant-driven (emic) and researcher-driven (etic) perspectives.

3. Coding is not a mechanical process.

4. Themes emerge through multiple coding cycles, requiring both inductive and deductive reasoning.

5. Analytic memos and negative case analyses strengthen the credibility of the findings.

# data[].attributes.body
I found this chapter very insightful in that it mentions examples and explanations of how to code, but the quote that stood out to me was the following:

“...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"!

# data[].attributes.body
I found the background of the author, Johnny Saldana, intriguing, as a film, dance and theater student, his research centers drama, education, youth theater curriculum and pedagogy and performance ethnography. Described as research from page to stage, Saldana's approach to ethnotheater and research is best understood as an interpretive act.

# data[].attributes.body
Lean coding is the process of forming a smaller number of codes rather than a larger number of codes in our analysis process. Lean coding results in five to seven themes that become the major headings in the findings section of the qualitative report (pp. 161).

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

# data[].attributes.body
Existing theory & Guiding research + Emergent Qualitative Data

# data[].attributes.body
Write a theme passage. This includes the codes used as evidence for the theme, including quotes to provide realistic descriptions of the situation, and uses different sources and people.

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

# data[].attributes.body
Have your own rules for coding:

o   Bold means X

o   Italics means Y

o   Highlighted in yellow means…

# data[].attributes.body
There is no “one way” to coding. There are many ways to code (open, line-by-line, chunking, fracturing, analytic, constant comparative). So...experiment!

# data[].attributes.body
I found this chart to be the most helpful in being able to thin about the coding process. Most of the time when I see diagrams in methods books, they are not as useful to me. Yet, I like how this shows a clear kind of process. But, I am also wondering about how to flip it. If I can enter it from different places and create a different type of map for the process as I would like to express a process that is more cyclical like the spiral diagram in the Tracy but clear like this one in the Saldaña.

# data[].attributes.body
In Creswell & Creswell Báez’s Chapter 18: Coding Text Data, the authors emphasize that coding is a crucial step in qualitative research, allowing researchers to organize, categorize, and interpret textual data systematically. Coding is not just about breaking down information but about identifying patterns, themes, and meaning within qualitative data.

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?

# data[].attributes.headline
Unpacking Saldaña’s Approach to Codes and Coding

# data[].attributes.subject
Unpacking Saldaña’s Approach to Codes and Coding

# data[].attributes.body
In The Coding Manual for Qualitative Researchers, Johnny Saldaña establishes coding as the critical bridge between raw data and meaningful interpretation. He defines a code as a word or short phrase that symbolically represents a portion of data, but emphasizes that coding is not a mechanical process—it is active, iterative, and interpretive. Unlike quantitative research, where data is often reduced to numerical values, qualitative coding requires reflection, intuition, and analytical flexibility.

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?

# data[].attributes.headline
Exploring Saldaña’s Fundamental Coding Methods

# data[].attributes.subject
Exploring Saldaña’s Fundamental Coding Methods

# data[].attributes.body
In The Coding Manual for Qualitative Researchers, Johnny Saldaña presents coding as more than just labeling data—it is a way of constructing meaning from lived experiences. Chapter 2 introduces first-cycle coding methods like Descriptive Coding, In Vivo Coding, Emotion Coding, and Values Coding, alongside second-cycle techniques such as Axial Coding and Pattern Coding. Each approach serves a different function: some preserve participant voices (In Vivo), others highlight emotions (Emotion Coding), while some uncover thematic connections across data sets (Pattern Coding).

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?

# data[].attributes.body
Sewart pointed to a quote by Patton, "In qualitative inquiry, the researcher is the instrument" The researcher is the instrument doing the analysis, unlike in quantitative data where there is a third component including surveys, questionnaires, or standardized tests. As the researcher and the research instrument, I am reminded of the importance of a researchers education and professional background in a field. This is a process that cannot be skipped as it directly leads to a researchers knowledge and skill accumulation. Reflexivity is also an important process for qualitative researchers to ensure their analysis and findings are credible. I am reminded that qualitative research is not attempting to measure data or outcomes, but rather attempting to provide an analysis of a phenomenon through the researcher as instrument.

# data[].attributes.body
Saldana points out that codes are not just taken directly from qualitative data, rather they come from the researcher knowledge, thought, and creativity. Codes are not just labels of words or thoughts that arise in qual data but they are created through an epistemological and ontological processes.

# data[].attributes.body
1. What is the data telling me?

2. What is it I want to know?

3. What is the dialectical relationship between what the data are telling me and what I want to know?

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

# data[].attributes.body
Stuart highlights the concept of “the researcher as instrument” in qualitative research, and thus, the importance of the researcher acknowledging their intra-individual identity. She states that because it is heavily dependent on the researcher’s skills, reflexivity is necessary.  Reflexivity acknowledges “the importance of self-awareness, political/cultural consciousness, and ownership of one’s perspective” (Patton, 2002, p. 64). Being reflexive then “is to undertake an ongoing examination of what I know and how I know it” (Patton, 2002, p. 64, emphasis in original). But reflexivity also means acknowledging that all this may change.

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.