While all these are important tools, they all also require a high level of engagement with the questions, and unfortunately, that is increasingly rare. And when you can't assure engagement, the most elegant tool ends up with worthless response data.
I find that to maintain engagement and still work through complex lists, a simple scale assessment of each choice still provides adequate analysis data. Occasionally, 2 simple scale assessments are needed, but that again requires more engagement.
Engagement is key for sure, but not all of these methods are as demanding as you're saying -- in my opinion, Pairwise Comparison is less taxing and a lot more interesting for participants than having to rating everything individually on an arbitrary scale (while also avoiding issues like scale bias and straightlining).
I've seen product teams collect great data from long complex customer surveys that combine multiple of these methods together in a single questionnaire, nevermind a simple survey that incorporates just one method.
If you have strong opinions against them, that's no problem, but I think you should reconsider a blanket view that they're all too taxing for people to be able to engage with them sufficiently. There's a range of complexity in the methods mentioned in this guide -- they're not all as busy as conjoint analysis!
While all these are important tools, they all also require a high level of engagement with the questions, and unfortunately, that is increasingly rare. And when you can't assure engagement, the most elegant tool ends up with worthless response data.
I find that to maintain engagement and still work through complex lists, a simple scale assessment of each choice still provides adequate analysis data. Occasionally, 2 simple scale assessments are needed, but that again requires more engagement.
Engagement is key for sure, but not all of these methods are as demanding as you're saying -- in my opinion, Pairwise Comparison is less taxing and a lot more interesting for participants than having to rating everything individually on an arbitrary scale (while also avoiding issues like scale bias and straightlining).
I've seen product teams collect great data from long complex customer surveys that combine multiple of these methods together in a single questionnaire, nevermind a simple survey that incorporates just one method.
If you have strong opinions against them, that's no problem, but I think you should reconsider a blanket view that they're all too taxing for people to be able to engage with them sufficiently. There's a range of complexity in the methods mentioned in this guide -- they're not all as busy as conjoint analysis!