[Limdep Nlogit List] How to deal with large numbers of attributes?

[Mikołaj Czajkowski]

Dear Richard,

It seems to me like the answer to this question would depend on the goal 
of the modeller - whether he wants to learn a lot about the most 
important attributes only, or have some idea about all the attributes. I 
am not sure is a lot of concrete advice can be given in these kinds of 
situations.

Cheers,
Mik


On 2018-02-16 21:20, Richard Turner wrote:
> Greetings,
>
> What is the best way to handle large numbers of attributes in discrete
> choice experiments?
>
> Is it better to do a *partial profile design* or to do some* two-step
> approach* such as conducting  an "initial study" using a partial profile
> design, then conduct a final study using the most important attributes,
> which were derived from the initial study (implicit in the second method
> would be to synthesize the learnings from both studies to get some ranking
> of all the attributes)?
>
> I've done some searching, but haven't found any "defining" papers on the
> subject.
>
> Any advice and/or direction is greatly appreciated!
>
> Regards,
>
> Richard
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