I'm currently facing a discrepancy between counts and utility estimates for one of my categorical attributes that I'm struggling to explain.
Per count analysis level 2 of said attribute is chosen disproportionately less often than the other 4 levels by a small but significant margin.
Level 1 37.62
Level 2 32.13
Level 3 36.95
Level 4 41.87
Level 5 41.21
In estimation, to my understanding, the first level is "dropped" to allow for identification of the model and anchor the utilities of the other levels (as the reference point with a utility of zero).
From that understanding, I would think it follows that - judging by the counts - the utility of level 2 should be lower than that of the reference level 1, no? (assuming there is no massive correlation in the design that couples level 2 with some other impactful attribute-level with large utility which I checked for and the standard errors are also well-behaved so that I don't think correlation is at play here)
However, estimation (within MBC tool, default effect coding w/ level 1 = 0 0 0 0 ) yields the following utility structure:
Level 2 0,525
Level 3 0,723
Level 4 0,753
Level 5 0,871
Am I missing something? Thanks in advance for any pointers!