Dear Sawtooth Team,
I conducted a CBC analysis in which one of the attributes is "numeric" with 5 levels and equal distances.
I used the hierarchical Bayes model (HB) from Sawtooth software to estimate a single coefficent for the "numeric" attribute - taking into account that I specified the value of the levels as single digits prior to running the model.
I was wondering, why it is important to specify the values similar to the level appearance which the participants see.
E.g.: Imagine the numeric values range from 10.99 to 15.99 with equal distances of 1. Hence, to code it in single digits, the values for the HB estimation would be 0.1099 - 0.1599.
To compare different models, I further specified the values with 1-5 (since the distance between the levels are equel). This leads to a better percent certainty and root likelihood but much different coefficients.
Another variables takes the levels of 100 - 200 with equal distances of 20. Hence, I would code the values 1, 1.2, 1.4,...However, again, specifying the values with 1-5, the percent certainty and root likelihood receives better fits.
Should I still stick to your suggestion and specify the values similar to the level appearance which the participants see or focus on the model with a better fit?
Thank you.
Best regards,
E.S.