I was supposed to read Wanderlust for my first year seminar, but it ended up being one of the first school assignments I did not fully complete. Three years later, my next creative writing teacher recommended A Field Guide to Getting Lost to all of us new graduates. I read it, my first Rebecca Solnit book, at the end of March this year. I immediately wanted to read everything Solnit had ever written, and shipped The Faraway Nearby home so my mother could bring it with her to France. I’m 50 pages from the end now and, like all her books, it’s a wondrous journey across many geographies, stories, and histories.
“Fairy tales are children’s stories not in who they were made for but in their focus on the early stages of life, when others have power over you and you have power over no one.
In them, power is rarely the right tool for survival anyway. Rather the powerless thrive on alliances, often in the form of reciprocated acts of kindness – from beehives that were not raided, birds that were not killed but set free or fed, old women who were saluted with respect. Kindness sewn among the meek is harvested in crisis.”
From the internet
The LSE Impact Blog shared a fun way to spread academic learnings to a wider audience: comics!
“By turning research into an illustrated abstract it’s possible to make academic work more accessible to a non-technical audience and people outside of the discipline. Alpa and Karen have even been approached by an author who would like to include the image in a forthcoming anthropology textbook, while Alpa hopes the article will also reach students, other academics and even non-academics, to explain the value of anthropology and long-term participant observation.”
My mother and I listened to “Episode 77: Kalevala (with Elena Varg)” from Spirits on the train to Versailles. The Finnish epic is bonkers, featuring birds nesting on the limbs of goddesses, the Devil’s own personal petting zoo, badass mothers, and a river of death. Elena Varg’s accent is wonderful and her excitement for the story makes this one of my favorite Spirits episodes ever.
Haribos Sour Rainbow Strips are one of a variety of rainbow-colored, sour/sugar-coated gummy candies, nearly all of which I love. While other brands can be too licorice-flavored, gummy, sweet, or unwieldly, Haribos’ colors can easily be stripped apart and each belt of tart candy is mouthwatering. Unfortunately unavailable in Nairobi. I should have brought more than one bag back from France, since I’ve already devoured the one I did bring.
Watch Them Again
You can really see the drawings behind the animation in the original Lion King movie. Rafiki is more bonkers than I remembered, and Nala a more beautiful lioness.
Also watched some Modern Family in the evenings with my mother on our vacation. Like most family drama/comedy shows I love (see also Reba), everyone makes mistakes and is flawed but they choose to do the right thing in the end. Heartwarming and cozy, with lots of silly in between.
I recently read Brené Brown’s Daring Greatly. The book presents Brown’s research, but it can feel more like a personal guidebook to tackling issues of vulnerability and shame.
Because the research has a conversational feel, it’s hard to understand how much of the book is based in research and how much in Brown’s individual experiences. She weaves in personal stories frequently, often to demonstrate a prickly emotional experience that was common across her interviews. But when I reached the end of the book, I wanted to know how she drew these theories from the data. I’ve only worked sparingly with qualitative data: how does one “code” qualitative data? How do you analyze it without bringing in all sorts of personal biases? How do you determine its replicability, internal and external validity, and generalizability?
Ingeniously, Brown grounds the book in her research methods with a final chapter on grounded theory methodology. Her summary (also found online here) was a good introduction to how using grounded theory works and feels. But I still didn’t “get” it.
So I did some research.
Brown quotes 20th century Spanish poet Antonio Machado at the top of her research methods page:
“Traveler, there is no path. / The path must be forged as you walk.”
This sentiment imbued the rest of the grounded theory (GT) research I did. Which seemed bizarre to a quant-trained hopeful economist. I’m used to pre-analysis plans, testing carefully theorized models, and starting with a narrow question.
Grounded theory is about big questions and a spirit of letting the data talk to you.
Founded by Barney Glaser and Anselm Strauss in 1967, GT is a general research methodology for approaching any kind of research, whether qual- or quant-focused. When using GT, everything is data – your personal experiences, interviews, mainstream media, etc. Anything you consume can count, as long as you take field notes.
Writing field notes is one of the key steps of GT: coding those notes (or the data themselves – I’m still a little blurry on this) line-by-line is another. The “codes” are recurring themes or ideas that you see emerging from the data. It is a very iterative methodology: you collect initial data, take field notes, code the notes/data, compile them into memos summarizing your thoughts, collect more data based on your first learnings, code those, compile more memos, collect more data…
Throughout the whole process, you are theorizing and trying to find emergent themes and ideas and patterns, and you should actively seek new data based on what your theories are. You take a LOT of written notes – and it sounds like in the Glaserian tradition, you’re supposed to do everything by hand. (Or is it just not using any algorithms?)
Brown describes the data she collected and her coding methodology:
“In addition to the 1,280 participant interviews, I analyzed field notes that I had taken on sensitizing literature, conversations with content experts, and field notes from my meetings with graduate students who conducted participant interviews and assisted with the literature analysis. Additionally, I recorded and coded field notes on the experience of taking approximately 400 master and doctoral social-worker students through my graduate course on shame, vulnerability, and empathy, and training an estimated 15,000 mental health and addiction professionals.
I also coded over 3,500 pieces of secondary data. These include clinical case studies and case notes, letters, and journal pages. In total, I coded approximately 11,000 incidents (phrases and sentences from the original field notes) using the constant comparative method (line- by- line analysis). I did all of this coding manually, as software is not recommended in Glaserian-grounded theory.” [emphasis mine]
The ultimate goal is to have main concepts and categories emerge from the data, “grounded” in the data, that explain what main problem your subjects are experiencing and how they are trying to solve that problem. For example, Brown’s work centers on how people seek connection through vulnerability and try to deal with shame in various health and unhealthy ways. She started with this big idea of connection and just started asking people about what that meant, what issues there were around it, etc. until a theory started to arise from those conversations.
You’re not supposed to have preexisting hypotheses, or even do a literature review to frame specific questions, because that will bias how you approach the data. You’re supposed to remain open and let the data “speak to you.” My first instinct on this front is that it’s impossible to be totally unbiased in how you collect data. Invariably, your personal experience and background determine how you read the data. Which makes me question – how can this research be replicable? How can a “finding” be legitimate as research?
My training thus far has focused on quantitative data, so I’m primed to preference research that follows the traditional scientific method. Hypothesize, collect data, analyze, rehypothesize, repeat. This kind of research is judged on:
Replicability: If someone else followed your protocol, would they get the same result?
Internal validity: How consistent, thorough, and rigorous is the research design?
External validity: Does the learning apply in other similar populations?
Generalizability: Do the results from a sample of the population also apply to the population as a whole?
GT, on the other hand, is judged by:
Fit: How closely do concepts fit the incidents (data points)? (aka how “grounded” is the research in the data?)
Relevance: Does the research deal with the real concerns of participants and is it of non-academic interest?
Workability: Does the developed theory explain how the problem is being solved, accounting for variation?
Modifiability: Can the theory be altered as new relevant data are compared to existing data?
I also read (on Wikipedia, admittedly), that Glaser & Strauss see GT as never “right” or “wrong.” A theory only has more or less fit, relevance, workability, or modifiability. And the way Brown describes it, I had the impression that GT should be grounded in one specific researcher’s approach:
“I collected all of the data with the exception of 215 participant interviews that were conducted by graduate social-work students working under my direction. In order to ensure inter-rater reliability, I trained all research assistants and I coded and analyzed all of their field notes.”
I’m still a bit confused by Brown’s description here. I didn’t know what inter-rater reliability was, so I had assumed it meant that the study needed to have internal consistency in who was doing the coding. But when I looked it up online, it appears to be the consistency of different researchers to code the same data in the same way. So I’m not sure how having one person do all of the research enables this kind of reliability. Maybe if your GT research is re-done (replicated) by an independent party?
My initial thoughts are that all GT research sound like they should have two authors that work in parallel but independently, with the same data. Each develops separate theories and then at the end, the study can compare the two parallel work streams to identify what both researchers found in common and where they differed. I still have a lot of questions about how this works, though.
A lot of my questions are functional. How do you actually DO grounded theory?
How does GT coding really work? What does “line-by-line” coding mean? Does it mean you code each sentence or literally each line of written text?
Do these ever get compiled in a database? How do you weight data sources by their expertise and quality (if you’re combining studies and interviews with average Joes, do you actively weight the studies)? -> Can you do essentially quantitative analysis on a dataset based on binary coding of concepts and categories?
How do you “code” quantitative data? If you had a dataset of 2000 household surveys, would you code each variable for each household as part of your data? How does this functionally work?
If you don’t do a literature review ahead of time, couldn’t you end up replicating previous work and not actually end up contributing much to the literature?
And then I also wondered: how is it applicable in my life?
Is GT a respected methodology in economics? (I’d guess not.)
How could GT enhance quant methods in econ?
Has GT been used in economic studies?
What kinds of economic questions can GT help us answer?
Should I learn more about GT or learn to use it in my own research?