Hi Prof. Bliemer,
I wanted to follow-up on this thread regarding my PCSD DCE. To reiterate, I am using five labelled alternatives (video, telephone, app, online, f2f). These are my attributes and levels: admin (0, 1), tech (0, 1, 2), duration (30, 60, 90, 120), and feedback (0, 1). Please note, ‘admin’ for alternatives app and online is always 0; and ‘tech’ for f2f is always 0 so I have removed them from the codes and candidate set. I am treating all attributes as alternative-specific parameters except for ‘feedback,’ which will be a generic parameter.
I have completed a pilot survey and am using Bayesian priors from the estimates obtained from the survey for my main DCE design. However, I keep getting an undefined d-error. I have seen many posts on this forum regarding undefined d-errors but cannot determine if the issue lies with my code or how I have defined my candidate set, and hence multicollinearity.
The codes are pasted below. For the candidate set, am I correct in indicating ‘feedback’ as generic in the candidate set spreadsheet? For example, I have named the column for the feedback attribute as ‘feedback’ in all alternatives instead of video_feedback, telephone_feedback, etc.
I have tried using different types of draws, but that didn’t work either. I also tried reducing the number of Bayesian priors from 11 to 9, but that also gave me an undefined d-error.
Ngene codes:
- Code: Select all
Design
;alts = video, telephone, app, online, f2f
;rows = 50
;block = 5
;eff = (mnl, d, mean)
;alg = mfederov(candidates = pcsd_pilot.csv)
;bdraws = Gauss (2)
;model:
U(video) = video[-1.42913]
+ b1_video.dummy[(n, -0.03656, 0.24705)] * admin_video[0, 1]
+ b2_video.dummy[0.39052] * tech_video[1, 2]
+ b3_video[(n, 0.38072, 0.33673)] * duration_video[30, 60, 999]
+ b4.dummy[(n, 0.12529, 0.10148)] * feedback[0, 1]
/
U(telephone) = telephone[-1.66766]
+ b1_telephone.dummy[(n, -0.12181, 0.25296)] * admin_telephone[0, 1]
+ b2_telephone.dummy[(n, 0.24292, 0.25143)] * tech_telephone[1, 2]
+ b3_telephone[(n, 0.42640, 0.31660)] * duration_telephone[30, 60, 999]
+ b4 * feedback
/
U(app) = app[-1.42343]
+ b2_app.dummy[(n, 0.09786, 0.28830)] * tech_app[1, 2]
+ b3_app[(n, 0.39204, 0.36730)] * duration_app[30, 60, 999]
+ b4 * feedback
/
U(online) = online[-0.85626]
+ b2_online.dummy[(n, -0.16117, 0.27827)] * tech_online[1, 2]
+ b3_online[(n, 0.05708, 0.35007)] * duration_online[30, 60, 999]
+ b4 * feedback
/
U(f2f) =
b1_f2f.dummy[-0.24854] * admin_f2f[0, 1]
+ b3_f2f[(n, 0.19027, 0.13373)] * duration_f2f[90, 120]
+ b4 * feedback
$
Thank you in advance for your help.
Kind regards,
Pakhi