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Dear Michiel,

Just following up on the above query to ask about the balance of a D-efficient design generated with priors from a small pilot study.

We conducted a pilot with 29 respondents (x5 choice tasks each).

I estimated an MNL model after removing observations from individuals who always ...

Thank you, Michiel and sorry about the belated reply.

I just observed that following 1m and 2m iterations using the above algorithm that there is a pattern in the correlations between the same attributes across two alternatives.

These correlations tend to be higher for the first few attributes ...

Thank you, Michiel.

That's very useful.

I appreciate d-efficient designs are aimed at minimising standard errors of parameters, not to eliminate correlations, but I am always a little cautious especially when correlations are high. I agree we can go with 0.5/0.6.

Having tried different runs ...

Hello Michiel and All,

May I kindly confirm that my code is also correct?

My experiment has two unlabelled alternatives with 9 attributes (5 @ 2 levels, 1 @3 levels and 3 @ 4 levels).


Design;
alts = alt1*, alt2*, none; ? The '*' checks for dominance and removes any observations
rows = 60;
block ...