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Hello Prof ! We got data from 100 people pilot study, so next step was to find out the parameters to use them in the equation for finding the correct sample size required for main study.
For reaching there, I used the mnl model equation on this data as :
mlogit choice i.AttribA i.AttribB i.AttribC i.AttribD i.AttribE i.AttribE
1) From here I got coeff values for each of these attributes (with one level as base whose coeffs are absent), are they sufficient as parameters for future sample size calculation? NOTE: none of the coeff values are coming as significant here, is that ok or normal?
[img][https://drive.google.com/file/d/1me7hQn ... haring/img]
2) Do i need to use <,cluster(respondent_id)> as option, after the above command.. given each respondent was shown multiple(8) choice tasks. If respondents answered multiple choice sets, should we use the cluster or panel options? I have seen some resources declare it to be panel dataset using cmset (for DCEs where one person is shown multiple choice scenarios at one point of time ) , can we regard such cases as panel dataset, given the DCE is conducted only once and not overtime?

1. In a pilot study you will often find that coefficients are not statistically significant because the sample size may be too low. 100 respondents is a reasonable size for a pilot study though, so often you would see more parameters that are significant. In your case, only 1 parameter is significant. The coefficient for price is not significant in your study, which is somewhat surprising because this is an attribute that a lot of people usually find quite important. Could it be that your price level range is relatively narrow and therefore not many trade-offs are made with respect to price?
Yes, these parameters suffice for sample size calculations, but these calculations will likely not be very reliable because your parameters have large standard errors.

2. I do not know the software you are using to estimate, I only know Apollo and Biogeme, so I cannot say which option to use. SP data is always panel data if a single respondent provides multiple observations. The panel nature indicates that observations come from the same respondent and therefore can account for correlations. So you would typically use the respondent id to group the data. This is mostly important when you are estimating a mixed logit model, but not when you are estimating a multinomial logit model (it should give you the same parameter estimates).

Michiel