Currently I am facing a conjoint problem for which I could need some advice for an appropriate approach.
Just think of a car interior in which we distinguish different areas, e.g. the seat or the steering wheel, and so on.
For those areas we got different attributes, e.g. for the area seat attributes like color, material, existance of seat heating, and so on. Those attributes consist of different levels, of course, like it is usual for a conjoint study (Color: black, white, green; seat heating: yes, no, ...)
So in total, we got a quite large number of attributes (about 25 to 35) that we can assign to 10 different areas. The number of levels for the attributes range from 2 to 6.
Now our objective is to get importances for the attributes but also for the global areas.
I wonder what could be an appropriate approach for this study - ACA, CBC or maybe a combination of those?
In some way it would be useful to only show attributes of one area when using CBC tasks, for instance. But as we also need the comparison of the areas, we cannot only use a partial profile CBC. But those are only my first thoughts.
Many thanks in advance for any hints or ideas