Sarah J Tracy 🏜 Profile picture
May 24 7 tweets 2 min read Twitter logo Read on Twitter
Many beginning #qualitative researchers are overly concerned that they are doing analysis "wrong". Here are some activities that can help people to think qualitatively & practice analysis in playful ways.
These and other techniques will be expanded in my 3rd edition qual book.
1. Using 10-15 items from your own personal library (of books, music, movies, recipes), sort them into a set number of categories and name them. Then discuss what themes you chose and why you chose them.
Re-categorize them into another set of categories. Do this as many times as possible with the goal of seeing how many kinds of categories you might have (for example, chronological categories, genre, how much you like them, their societal popularity, etc.).
2 Check out Waite (2011) on using a deck of cards to understand analysis. Several key steps are: Sort a deck of cards (e.g., a data set); provide an explanation for why you sorted them this way (usually difficult because our common sense is tacit); (cont) journals.sagepub.com/doi/pdf/10.117…
(cont from above) then sort the deck in a new and way and explain the rationale (usually easier because the theorizing is more explicit) ; figure out what to do with the jokers and blanks (similar to data outliers).
3 Pose a simple question to a group of people who can respond w/ text or images. Then, use an online digital collaboration platform like Miro.com as a virtual whiteboard to work through the sorting process of how different aspects of texts or images can be grouped.
Note: These are starting points that especially helpful for newbies. They are not ending points. Good qualitative research requires interpretation, theorizing, and creativity. Said another way, please don't take these activities to suggest that sorting = analysis.

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More from @SarahJTracy

May 16
#Qualitative Peeps. From my very little playing around with it, it seems that AI can fundamentally impact qualitative data analysis and coding. If you or someone you know has used it systematically in qual analysis (or written about it), will you let me know?
In fooling around w/ ChatGPT w/ #qualitative analysis, I asked, "what is the sentiment of the following data?" [inserted deidentified data] & then, "what are the three main themes in the following conversation?" I found the descriptive summaries useful & astonishingly on point.
I then asked, "what are some theories that could help explain these themes?" And several were provided that make sense given the analysis we conducted. This experiment was with data that I've already collected and analyzed in the past.
Read 5 tweets
May 8
#Qualitative research is outstanding for understanding WHY.
BUT this does not mean that asking "why" questions in interviews is always the best way to go.
So what's the problem with "why" and what can researchers use instead?
🧵
The problem with "why" interview questions is that, when you ask people "why", they often become defensive, or simply double down on their convictions with a lack of reflexivity. What's more they, often philosophize.
A better avenue for interviewing can be to ask "how" instead of why.
For example:
How does that work?
How did you come to have that belief?
How is it that you came to engage in _______ behavior?
Read 5 tweets
Aug 28, 2022
Trying to contrast literature review (LR) from theoretical framework (TF)?
The LR synthesizes existing knowledge re. the research problem.
The TF provides a lens for analyzing & explaining the data.
A good research study must ADVANCE the literature but usually only USES the TF.
E.g.,
1) a Foucauldian analysis (TF) of emotional labor (LR).
2) identifying functions of humor (LR) using sensemaking theory (TF)
3) understanding work-life balance (LR) through a structurational lens (TF)
Knowing at least some of the related literature at the beginning of a study is fundamental for research design, appropriate research questions, etc. Most folks don't want to spend years of their life illuminating a problem that already has a clear solution. But...
Read 5 tweets
Dec 30, 2020
Just did an assessment of our #health #finances. After a ten-year personal experiment with a health savings account (#HSA) and our employer's high risk insurance option: GOOD CHOICE. I'm definitely not an expert, but if you're curious, here's our experience (a thread):
Our life situation made us right for an HSA. I was single for half the first 5 years, married for the 2nd, when married, both of us on HSA. No children, overall pretty healthy. Here's an article about who an HSA is good for: mayoclinic.org/healthy-lifest…
I deducted the maximum savings every year from my employer (which I believe was about $6,000). This went into our HSA. For medical expenses, if we had the extra cash, we used it rather than our HSA (as the HSA grows exponentially tax-free, which is a freaking good deal).
Read 9 tweets
Aug 12, 2020
A few ideas for how to create space for the communication of suffering and other vulnerable emotions, and to connect with students and employees as multi-faceted human beings in synchronous virtual meetings and classes. #compassion #academicchatter #orgcom A thread. 1/13
Arrive a few minutes early w /all your necessary materials prepared. Then, just as participants join, briefly and actively greet them. Just saying "Hello Kali. Good morning Joe" let's people know that they've been recognized. They are not invisible. 2/13
Allow space for (but do not mandate) non-agenda or task driven discussion. One option is by placing folks in 2-3 person break-out rooms for warm-ups and breaks. Folks should feel free to do whatever they'd like...chat, check email, tune out, go grab a cup of coffee. cont. 3/13
Read 14 tweets
Aug 6, 2020
One of the main things that stops good #qualitative research from being GREAT is that people get stuck during that ephemeral step between coding/thematizing and interpretation/claim-making.😩

Some musings and tips to help in this process:
As @ProfWay has pointed out, for most qual researchers, line-by-line coding is not necessary or appropriate on ALL of the study's empirical materials.
Choose a portion of your data (I recommend a maximum variation) for emergent coding, then check whether the codes are answering your RQ. If so, create a codebook & begin laying codes on top of the data. Do they work? Great. If not, go back & adapt. This is the iterative approach!
Read 11 tweets

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