Number of rows and interpretation of efficiency measures

[twickr07]

Hi

I am doing an unlabelled d-efficient design, with 6 attributes (3 continuous, 3 categorical) see code below.

Code: Select all

Design
;alts = alt1*, alt2*
;rows = 12
;eff = (mnl,d)
; model:
u(alt1)=  b1[0.00001]*effect[30,50,70]+b2[-0.0001]*speed[6,10,14]+ b3[-0.0001]*flare [10,40,60]+b4.dummy[0|0]*route [0,1,2]+b5.dummy[0|0]*moderate[0,1,2]+b6.dummy[0]*severe[0,1] /
u(alt2) = b1*effect+b2* speed+ b3*flare +b4.dummy*route +b5.dummy*moderate+b6.dummy*severe
$

1) Is there a way to check if the number of rows I'm using is appropriate?

https://drive.google.com/file/d/1ZHt6VcvfuuRO4VzByGXo3RSeG75ZN4Lq/view?usp=sharing

2) I've attached my MNL efficiency measures output, i'm struggling to interpret if the D-error, A-error and S-estimate is within the acceptable range? is there a rule of thumb for these efficiency measures?

Thanks a lot in advance!

Tara

[Michiel Bliemer]

1. The minimum number of rows should be at least #parameters/(#alternatives-1), which in your case is 8/(2-1) = 8. Therefore, 12 is enough in order to estimate the model. Ngene automatically checks for this, if you try ;rows = 7, Ngene will tell you that you need a minimum of 8. Note that while 12 is theoretically enough, it is often not a bad idea to have a bit more variation in your data. So you could decide to increase the number of rows and block the design, e.g. ;rows = 24 and ;block = 2, such that there are two versions of the survey with 12 choice tasks each.

2. The D-error and A-error have no meaning except that we want to minimise these values. You cannot tell by the value whether it is good or bad. If the D-error is Undefined/Infinite, then the model parameters are not identifiable, but if Ngene generates a finite D-error the model can be estimated. The S-estimate has a meaning and refers to the minimum sample size needed to estimate all parameters statistically significant at the 95% confidence level. However, the S-estimate is only meaningful if you have appropriate priors, e.g. that come from a pilot study. In your case, you should ignore the S-estimate. Your D-errors and A-errors look perfectly fine.

Michiel