I tried to segment my customers based on their part-worth utilities (zero-centered diffs) and have a question concerning the F ratios.
For example, I obtain a very high reproducibility for a 3-cluster solution, but the F ratio is lower as in case of a 2-cluster solution. Should I choose the solution based on the reproducibility or on the F ratio?
Furthermore, how can I calculate the F ratio for a specific variable? For instance, in your CCEA Manual on page 25, how do I arrive at an F ratio of 58.52 for the variable "Acceleration time 0-"? In case of part-worths, how can I calculate an F ratio over 3 attribute levels, i.e., an F ratio for the overall attribute (Level 1: 50, Level 2: 30, Level 3: 10 - what is the F ratio for the overall attribute)?
Finally, which clustering validation method do you recommend? I calculated the silhouette as suggested by Retzer but end up with an average width of approx. 0,25 - even though my ensemble is highly diverse and achieves an adjusted reproducibility of virtually 100 percent.
Thank you very much for your answers and your help!