Appreciate that this video goes a bit deeper to remind me of the "academic stuff" I've forgotten after being out of college for years. Like inductive vs deductive, semantic vs. latent, and splitting vs. lumping as approaches. These little nods to terminology really helps me continue searching through other resources to refresh my knowledge. Many other resources just go over the surface-level steps, but I already know those. I want discourse on the potential traps, the different schools of thought. Like for example, I'd love to hear your thoughts on when/why you might choose to cluster into single-tag themes as opposed to multi-tag themes. I've seen a number of folks talking about using tags such as 'motivation' or 'pain-point', and then clustering by those. That's similar to (but slightly different) from your example of tagging according to content and then clustering in to a 'pain-point' theme. I've done it that way where they're just tagged as a pain-point and I group them together, then I'm able to easily report out a summary of "here were the top pain-points mentioned', but I'm losing that second layer of coding provided in your example. Are there pros/cons to using one approach or the other that you've seen?
Good to see the video resonated and reminded you of content that is hopefully useful! On your question on single-tag vs. multi-tag themes, you're right - we're losing a layer of detail... but sometimes that's exactly what is needed. Which is why when deciding on single vs multi we often go back to the research objectives. Single-tag can be great for quick, high-level insights while multi-tag shines when you need more nuanced understanding. You see I'm going with the classic "no one-size-fits-all" answer here :) I've been thinking of a video that is more of a case study type, may come back to this point then
Very Helpful video, Thanks alot and keep on giving more content please! Would love to see a video about how research reports and how to give proper design recommendations
great content. Would love for you to speak abt the state of Qual research profession, given the downsizing of teams recently and general trend of reliance on quant data
In a next video, could focus more on the 5th step? For me, that's actually the most difficult part. Because for me the "insights" I can gather are just observations, just the actual findings. And I know Insights are more than that and they have a certain structure. But I don't quite get it. I did this for a project, all the tagging and clustering, and in the end what happened is that I understood the problem deeply. But then the phase of writing actual Insights gets difficult to me. Not difficult, but I see it like very obvious, they don't reveal nothing new. I mean, if you read the interviews you learn exactly the same things as if you read the insights directly. So I don't actually get the point of generating insights. What makes an insight an insight?
Ah this is an interesting point that you're making - yes insights are more than just observations or direct findings! Insights are "interpretations" that synthesise multiple data points. So it reveals patterns, motivations or needs that aren't immediately obvious from reading individual transcriptions. I'm hoping to make a case study video that will dive deeper into how to define and shape insights, hope this may help clarify this topic some more!
Appreciate that this video goes a bit deeper to remind me of the "academic stuff" I've forgotten after being out of college for years. Like inductive vs deductive, semantic vs. latent, and splitting vs. lumping as approaches. These little nods to terminology really helps me continue searching through other resources to refresh my knowledge. Many other resources just go over the surface-level steps, but I already know those. I want discourse on the potential traps, the different schools of thought.
Like for example, I'd love to hear your thoughts on when/why you might choose to cluster into single-tag themes as opposed to multi-tag themes. I've seen a number of folks talking about using tags such as 'motivation' or 'pain-point', and then clustering by those. That's similar to (but slightly different) from your example of tagging according to content and then clustering in to a 'pain-point' theme. I've done it that way where they're just tagged as a pain-point and I group them together, then I'm able to easily report out a summary of "here were the top pain-points mentioned', but I'm losing that second layer of coding provided in your example. Are there pros/cons to using one approach or the other that you've seen?
Good to see the video resonated and reminded you of content that is hopefully useful!
On your question on single-tag vs. multi-tag themes, you're right - we're losing a layer of detail... but sometimes that's exactly what is needed. Which is why when deciding on single vs multi we often go back to the research objectives. Single-tag can be great for quick, high-level insights while multi-tag shines when you need more nuanced understanding. You see I'm going with the classic "no one-size-fits-all" answer here :) I've been thinking of a video that is more of a case study type, may come back to this point then
Very Helpful video, Thanks alot and keep on giving more content please! Would love to see a video about how research reports and how to give proper design recommendations
Thanks for the comment :) On the topic of research reports, have you seen this video? ruclips.net/video/fOlvYYR0oh0/видео.htmlsi=1i2vsOh_gWNbLDWS
The video is really great, I learned incredible things that I had no idea existed.
really hope your channel gets more subscribers, your content is so good, you deserve it! keep up :)
Ahh really happy to read your comment, thank you!
M happy you are back 🎉
Great content man, greetings from Brazil
Good to see you back :)
great content. Would love for you to speak abt the state of Qual research profession, given the downsizing of teams recently and general trend of reliance on quant data
Yes, great shout - I've been collecting some thoughts on this topic so hopefully should be able to produce a video on the topic 👍
In a next video, could focus more on the 5th step? For me, that's actually the most difficult part. Because for me the "insights" I can gather are just observations, just the actual findings. And I know Insights are more than that and they have a certain structure. But I don't quite get it. I did this for a project, all the tagging and clustering, and in the end what happened is that I understood the problem deeply. But then the phase of writing actual Insights gets difficult to me. Not difficult, but I see it like very obvious, they don't reveal nothing new. I mean, if you read the interviews you learn exactly the same things as if you read the insights directly. So I don't actually get the point of generating insights. What makes an insight an insight?
Ah this is an interesting point that you're making - yes insights are more than just observations or direct findings! Insights are "interpretations" that synthesise multiple data points. So it reveals patterns, motivations or needs that aren't immediately obvious from reading individual transcriptions. I'm hoping to make a case study video that will dive deeper into how to define and shape insights, hope this may help clarify this topic some more!