I am currently trying to create clusters from the data I gathered in my ACBC. The thing with my HB estimates is, that price overall has an extremely high importance on average (50%) while the rest of 7 attributes shares the other 50%. This is probably based on the fact, that the price range I chose for the product analyzed covers the full-range from cheapest to highest prices in the market, consequently the majority of respondetns has strong negative utilities for the upper price range.
This however becomes an issue with the clustering: if I run an analysis on all levels including the pricebreakpoints, the resulting f-statistics from the clustering for the different levels are varydrastically and especially price is extremely high. to give you an idea for 3-group cluster:
for non-price levels (24), the f-statistics is avg. at around 30 and ranges from til 60. for price breakpoints (10) it ranges from 1 to 500 (!) and has average of roughly 200.
If I get the concept of f-statitistics correct, then it says how much a particular variable (level) influences the clustering. If I now take my values, the clusters should be somewhat random on non-price levels and basically price is the only differentiator as here, partworth difference are the highest.
I am not sure if these results are useful or if it may make sense if I took the results from a run without price. These clusters (wihtout price) are somewhat more diverse on the non-price levels and better interpretable I would say. I am scared however, that if I used this solution, then I may disrespect the fact, that for this product price is a main driver as utilities vary tremendously and ignoring this attribute may give me "crude" results.
So, is it ok to leave out attributes that were used in the ACBC and for the HB estimation during the clustering? Especially when this attribute (price) has the by far highest importance? Wouldnt clusters in this case have extreme variances on the price levels, and would this be a "valuable" result?
Many thanks Lukas