choice-metrics.com

One more question.

We have 4 blocks (4 scenarios in each) generated in Ngene. We mixed up, and only used 3 of the blokcs, the last block (Block D) we mixed with an earliger generated outcome (however same syntax and design), just another "run".

Can we include the last block? And use all the ...

Thanks again!

So then another problem appears.
When we try to run regressions with only one block, then half of the parameters gets omitted because of collinearity.. Is this because we have to little variation with only 4 scenarios?

Nice!

Thanks for replying!

The choice-sets/scenarios generated from Ngene is correct in the 4th block, however we mixed up and used some old generated scenarios, which makes the 4th block just totally different...

So the question is: when you have 16 scenarios, divided in 4 blocks, 3 of the ...

Hi!

I posted in the other forum what maybe should have been posted here..

We have done a 3000-respondent survey including a DCE.
But now we have discovered that wrong choice-sets have been used ...

So:

1. We had 7 attributes, and runned a eff(mnl, d) with signs as priors (-0,00001 / 0,00000 ...

Hi!

What happens if we use wrong priors in Ngene (only sign => example.: -0,000001), in other words: wrong sign!

And this happens on half of the attributes in the model..

Will the data be totally unusable? Or what is possible to use? How does Ngene use take use of the priors we give?

-Ben

Thanks!

When only using +/- for priors, what would be best in Ngene, orthogonal 16/4 or efficient design 16/4, or both (i.e. orth=seq + eff=(mnl,d)) ?

Hi!

When using an efficient design, with only signs for parameter (i.e. [0.000001])

Is it possible (necessary) to block? Want to maximum give 4 choice sets to each respondent, but need at least 13 choice sets. Block in 4 from 16 ?
Or is blocking only from orthogonal?

I quote from a "How to DCE" for the World Bank and WHO

To estimate trade-offs, a continuous attribute must be included in the DCE. Within the job choice literature, this continuous variable is commonly salary. Inclusion of this attribute allows estimation of how improvements in aspects of a job ...

Thanks!

Yes all are positive, (never done corruption)..

I know all signs, should I use 0.000001 OR 0.01
When I use 0.01 the d-error is 0,2 but S = 900..

Yes, I was talking about the actual survey with Bayesian :)

The pilot is done, but we are changing attributes, so the only thing we know is sign!

QUESTION: Do we then use eff design or Bayesian if we only have sign?

Is this good for that?:


Design
;alts = President_A*, President_B*
;rows ...