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Kindle Notes & Highlights
by
Sam Ladner
Read between
January 29 - January 31, 2020
Stories starring a customer (and not the CEO) have a way of eliciting what ethnographers call the “emic” position, or what business people would call customer obsession. Isn’t it odd, then, that we don’t use this universal way of sharing knowledge when we share knowledge about customers? Sadly, stories don’t provide what many believe to be essential for research: the scale of any incidence or why something causes something else.
Using ropes and people, these researchers were able to make a replica statue rock back and forth and “walk” (Lipo, Hunt, & Haoa, 2013). Maybe the stories and the science are both right. Stories and science give us different things.
How did their employees cope with unexpected problems? How does a community move 4.35 metric tons of stone? Quant research stops short of this. Quantitative researchers focus on scale on causation but fail to provide coherence and participant focus. Qualitative research, by contrast, focuses on coherence and participant focus but lacks scale and causation.
When we use quant data, we put the sensemaking onus on the consumer of the research, and when we use qual data, we sacrifice scale and causation. This is a terrible trade-off.
I explain that stories are the way to learn what participants value, and for researchers to gain the emic perspective (that is, to focus on what the participants consider important, not what the researcher, the product manager, or even the CEO thinks is important).
Telling stories is how you cohere thick description. Your participants become real, intelligible, sympathetic, and the protagonist in whatever it is your organization is doing. No longer a bit-player, the customer takes center stage in qual research.
narrative analysis, a research method that dissects stories and focuses less on what happened and more on how individuals come to make sense of what happened
The word empirical simply refers to direct observation, so quant data can be subjective or empirical, and qual data can be either subjective or empirical.
At their core, these two approaches have differing belief systems about how knowledge is created (epistemology) and even more fundamentally, about what is reality itself (ontology).
As summarized in Table 1, quantitative research begins with the assumption that reality is a stable, objective thing that can be understood and observed. If you have this assumption, it makes sense that you use the scientific method when you create new knowledge.
Objectivists tend to adopt what methodologists call “positivism,”[3] or the belief that the human world is just as discoverable through observations as the natural world is.
In contrast to objectivist quant researchers, qualitative researchers typically believe that our social reality is constructed, which means the human world is not “real” in an objective sense, but based on everyday interpretations humans make when they go about their business.
It is only through repeated social experience, or what sociologists Berger and Luckman call “reification,” that we come to believe that the social world is as stable as the natural world.
The goal of constructivist ontology is to understand the process by which people understand their social reality.
Interpretive flexibility is why users hack or mod their tools in ways their designers never intended, and it is why usability testing alone does not reveal the full picture of how technology will get adopted.
Objectivist researchers may observe “technology use” and take it for granted that people want to complete tasks more quickly. But constructivists may begin to ask, “How do humans interpret this technology?”
Data collection for objectivists is straightforward and deductive: did this finding confirm or deny my previously held belief? But for constructivists, inductive approaches are more familiar. What do these humans think this tool is, metaphorically? What do my participants believe about productivity or getting things done? How do they grapple with getting things done faster, but then having more things to do? How does their experience trouble our belief that being faster is always better? These
Quantitative researchers focus more on scale and causation, and like to have replicability and precise measurement. This differs significantly from qualitative researchers, who concern themselves with describing richness of context, the nature of change, and having empathy for participants.
Losing faith in the scientific method is quite freeing when you are in the early stages of trying to understand something.
Help your stakeholders luxuriate in the people they are making things for. Give them permission to fall in love with their customers. Show how participants come to believe a certain thing. Describe the workarounds they employ to complete certain tasks. Lay bare their thoughts, dreams, motivations and hidden beliefs that explain why they are not making so-called rational choices.
Show causation in a different way: how people interact with objects, social structures and other people, and how they make decisions.
Explanation is perhaps the most underrated strength of qualitative research!
Time does not permit in-depth qualitative research Time does not permit in-depth quantitative research Negotiating access to participants is challenging Finding a large data set is challenging
Bryman (2006) outlines five methodological reasons for mixing methods: Complementarity: deepen or enhance other data Expansion: expanding the inquiry to ask different questions Development: use one method to inform and improve the other Triangulation: corroboration of earlier data Initiation: resolving earlier contradictory findings
In this stage of the project, your goal is to give everyone a clear, shared understanding of what the project will achieve. This entails learning your stakeholders’ needs, detailing the resources at your disposal, and then describing your approach to best meet those needs and resource constraints.
Shared beliefs and the artifacts that symbolize them are a key component to effective collaboration.
It is important to listen to what your stakeholders say they need, even if you know for a fact you cannot deliver their research wish list.
This reciprocal dynamic indicates that you are listening, considering and involving, but also advising and directing.
Schedule time to listen to what they want, and then even more importantly, repeat that back to them in concrete ways.
Your role as a research leader, therefore, is to engender a climate of psychological safety, to prepare your stakeholders to have realistic expectations, and to act as promulgator of those shared beliefs.
Producing a shared artifact is key to ensuring a shared belief about this process.
