Dear Sawtooth team,
I conducted a CBC analysis in which two attributes are of "numeric" nature. The attributes consist of 5 levels each and have equal distances between each level.
I estimated a hierarchical Bayes model (HB) from Sawtooth software to estimate a single coefficient for each "numeric" attribute - taking into account that I specified the value of the levels as single digits prior to running the model.
Due to the nature of the attributes both linear attributes were coded equally (namely from 1 – 5) in the X matrix.
The estimation results result in significantly different utility coefficients (as expected). However, interestingly, the reported standard errors for both coefficients are exactly the same.
Does this make sense? Is this the case since the levels were coded equally in the matrix? If I change the coding in the X matrix for one attribute, the standard error change (obviously).
Looking at the relative importance, the choice simulator as well as First Choice Hit Rates, I observe plausible results and significant different preferences for the two numeric attributes.
Estimating the model using a part-worth model (as a robustness check) leads to plausible results as well.
Still, I am wondering why I observe the same standard error for the two utility coefficients?
Thank you and best regards,