Don’t call it AI: Turn words into numbers with quantitative ethnography
Summary
Quantitative ethnography is the niche subfield you’ve never heard of, but it’s one you’ve been increasingly pressured to practice in over the past couple of years. It’s the math that turns words into numbers underlying generative AI, and LLMs have been getting in between you and a radically new approach to working with verbatims, transcripts, and other texts. Business stakeholders are always pushing for greater efficiency, faster turnarounds. Qualitative researchers are always looking for more contact with users, and greater engagement with findings and reporting. Quantitative ethnography (and epistemic network analysis) offers a compromise: by trading structure and semantics for human sensemaking in the analysis part of research, perhaps both groups can get what they want. I’ve had the opportunity to conduct quantitative ethnographic analyses in enterprise studies involving dozens of products, and impacting hundreds of thousands of end-users. Stakeholders were willing to accept a different kind of analysis, and engage more deeply with the process, in exchange for quicker answers. In this talk, I’ll share how quantitative ethnography differs from qualitative ethnography, the tradeoffs you’ll have to make, and the kinds of results you can expect. This isn’t a tools talk, but you won’t need to do any math, either. I’ll close with a look into the near future, one where you can talk with as many users as will take your call with effectively zero additional analysis work; where you can have the analysis running live during your session, and have the user participate in the sensemaking process on-the-fly; and the dream of every product manager, one where stakeholders can have dashboards of evidence updated live as users talk.
Key Insights
-
•
Quantitative ethnography unifies qualitative ethnographic methods with quantitative statistical validation, avoiding typical mixed-methods back-and-forth.
-
•
Formalizing coding rules in a detailed code book is essential to scale qualitative insights and enable automation.
-
•
Defining mechanistic signifiers, such as keywords or phrase rules, is necessary to automate qualitative coding effectively.
-
•
Intra-sample statistical analysis uses each coded line as a data point rather than each respondent, enabling meaningful stats from small sample sizes.
-
•
Partnering with data scientists is critical because quantitative ethnography requires specialized, adjusted statistical methods that differ from conventional ones.
-
•
Researchers must regularly validate coding accuracy and statistical assumptions over time, a process called closing the interpretive loop.
-
•
Quantitative ethnography can scale from a handful of interviews to thousands of verbatim responses, maintaining rigor at all scales.
-
•
Epistemic network analysis helps identify and quantify relationships between qualitative codes within the text data.
-
•
Large language models can automate parts of quantitative ethnography but require sacrificing some control over code definitions and initial synthesis.
-
•
Quantitative ethnography opens the possibility for near-real-time insights by automating coding and saturation metrics during ongoing data collection.
Notable Quotes
"Business stakeholders push researchers for faster turnarounds and numbers, often favoring surveys over deep interviews."
"Quantitative ethnography isn’t mixed methods; it’s a unified method using both qualitative theory and quantitative validation."
"If you can’t come up with a rule for something, you can’t code it."
"Each coded line is a data point, which enables statistical power even with small numbers of respondents."
"Partner with data scientists to pick and adjust statistical tests because quantitative ethnography requires new assumptions."
"Closing the interpretive loop means regularly checking that your coding and stats hold up as new data arrives."
"Epistemic network analysis reveals meaningful connections between codes, suggesting but not proving why ideas cluster."
"Large language models cluster text using semantic relationships rather than shared vocabulary like traditional QDA."
"Using generative AI math lets you skip stats, but you lose control over what codes start your synthesis."
"If rules and stats update in real time, you could know when saturation is reached as data streams in."
Or choose a question:
More Videos
"UX practitioners already have the mindset and mentality needed to drive a product discussion forward and a handy toolkit of approaches and methods."
Vicky Teinaki Michele Marut Tim ParmeeShort Take #3: UX/Product Lessons from Your Industry Peers
December 6, 2022
"We had an engineering culture that confused uniformity with design, and consistency with design."
Phil GilbertA Consistent Culture of Design
May 14, 2015
"If you’re generating buy-in, don’t forget what it will mean for your difficult stakeholders—they may have external pressures you don’t know about."
Darian DavisLessons from a Toxic Work Relationship
January 8, 2024
"Safe money has solid returns, so fiscally responsible companies demand higher returns from new investments especially with inflation."
Mike OrenWhy Pharmaceutical's Research Model Should Replace Design Thinking
March 28, 2023
"To avoid bias in teams, it’s easier to gravitate toward what’s comfortable, but we need voices that challenge us."
Joi FreemanA New Vantage Point: Building a Pipeline for Multifaceted Research(ers)
March 30, 2020
"Connection is the energy that exists between people when they feel seen, heard, and valued."
Alla WeinbergCross-Functional Relationship Design
December 6, 2022
"Kay Abba is a business and project analyst, UX designer, and project manager who brings multifaceted expertise to our note-taking."
Bria AlexanderOpening Remarks
September 30, 2021
"We basically wrote an HCD book with 25,000 words of notes for the training workshop."
Elena Naids Liza McRuerThe Power of Difficult Conversations: A Case Study on How We Introduced Design Ops in the Federal Government Space
October 2, 2023
"Southwest Airlines focused on quality of experience and recorded 24 straight years of profitability despite never dominating market share."
How to Identify and Increase your "Experience Quotient"
June 15, 2018