Hi Xavier,

thanks a lot for your (and sorry for my late!) response.

Yes, sure. Agree. There might be relevant traits I am not aware of. Anyway I think it can help to have some of these general traits and to be able to filter the research results. Maybe there’s a cluster (based on any kind of traits combination). If there’s no cluster there might be some other characteristics that might lead to a cluster or specific pattern. Or there is no cluster at all, also possible…

We plan to collect some basic traits (as described) in addition with an ODI-ish user needs evaluation to be able to apply general filters. Something like the difference with the gloves cannot be prepared and asked in advance, of course.

The K-means clustering sounds interesting. Thanks for the hint! I’ll have a look at it. Finding clusters by applying some filters is something we also want to do. And then — of course — you have to dive deeper, eventually ask some test persons for specific feedback or an interview to understand why they responded as they did.

Regarding the “factor reduction”: Yes, that makes sense. I assume that you always have to presort and reduce potential outcomes that shall be evaluated. There are always more needs that should be evaluated and questions you want to ask. You have to preselect. That’s one point where mistakes can happen (when you select the wrong ones / btw: The same can happen to a music band when they select the wrong songs for their album ;-).

Yes! Your feedback helps. Thanks!

We currently set up a research approach based on similar ideas. Maybe I can share it someday here. And maybe we can discuss this as well. I’m always curious about other opinions and critical feedback. :-)

Thanks again for your insights!