Michiel Bliemer wrote:1. Yes
2. The range should be as wide as possible while keeping it realistic.
3. Then your WTP measure will also not be statistically significant (i.e., have a very large standard error).
4. It is difficult to design data specifically for models other than MNL, so it is common to design the data for estimating the MNL model but you can test your design for estimating the RPL model. You can test for this in Ngene by specifying multiple models and only optimise on the MNL model but allow evaluation for other models, e.g.
;eff = mnl_model(mnl,d)
;model(mnl_model):
...
;model(rppanel_model):
...
Of course you will need priors for the RPL model, which are often difficult to obtain from a pilot study.
Michiel
Many thanks Michiel,
Regarding your reply,
1. Regarding previous Q2: What if the consumers only make trade offs between price, and ignore other attribute, if the range between prices is too large? For example, I set price attribute as $10, $20, $30, and $40. Then, come up with a choice task that with Alternative 1: $10, comparing with Alternative 2 which is $40. In this case, I guess most the common consumers may choose Alternative 1, because of cheap and ignore other attributes.
2. Regarding previous Q4: So that means, no matter which model or models I am going to use for my data analysis, the Ngene design are exactly the same, right?
For example, I planed to use the RPL model for my study when I am designing the questionnaire, however, after I collected the data, I come up with a idea that to use both MNL, RPL and ELC, or other models together. So, in this situation, the same design (Ngene syntax) for DCE questionnaire in Ngene and the same data which have been collected, can be used for analyzing different models, right?
Many thanks Michiel,
Best regards,
Lohas