Pilot study & Bayesian D-efficient design

[Ethannn]

Dear Michiel,
I hope this message finds you well. I recently conducted a pilot survey based on the Ngene code you kindly provided earlier, involving 72 respondents. Thank you again for your valuable guidance.
I now have a few follow-up questions that I would greatly appreciate your insights on:
1. When estimating the MNL model, should I use dummy coding or effects coding for the categorical attributes? I ran both specifications and obtained different results:
o Using dummy coding, the MNL estimates are Mode1;
o Using effects coding, the estimates are Model2.
2. In Model 1, the signs of the attribute coefficients are all consistent with expectations, and most of them are statistically significant. However, the ASC coefficient is not significant. Will this affect the reliability of the model or the subsequent Bayesian D-efficient design? Based on Model 1, I prepared the following Ngene code for the Bayesian D-efficient design:
Design
;alts=alt1*, alt2*, alt3
;rows=24
;block=3
;eff=(mnl,d)
;model:
U(alt1) = ASC[0.219]+A [0.882]*A[1,2,0]
+ B [0.432]*B[1,2,0]
+ C [0.246]*C[1,2,0]
+ D [0.523]*D[1,2,0]
+ E[-0.00249]*E[50,100,150,200] /
U(alt2) = ASC[0.219]+A [0.882]*A[1,2,0]
+ B [0.432]*B[1,2,0]
+ C [0.246]*C[1,2,0]
+ D [0.523]*D[1,2,0]
+ E[-0.00249]*E[50,100,150,200] /
$

3. In Model 2, one of the attribute levels is not statistically significant. Would this non-significance compromise the suitability of the model for deriving prior parameters for a Bayesian design?
Design
;alts=alt1*, alt2*, alt3
;rows=24
;block=3
;eff=(mnl,d,mean)
;model:
U(alt1) = A.dummy[(n,1.440,0.177)|(n,2.012,0.195)]*A[1,2,0]
+ B.dummy[(n,0.912,0.182)|(n,0.890,0.158)]*B[1,2,0]
+ C.dummy[(n,0.434,0.169)|(n,0.754,0.169)]*C[1,2,0]
+ D.dummy[(n,0.111,0.164)|(n,0.981,0.154)]*D[1,2,0]
+ E[(n,-0.00209,0.00121)]*E[50,100,150,200] /
U(alt2) = A.dummy*A + B.dummy*B + C.dummy*C + D.dummy*D + E*E /
U(alt3) = ASC[(n,0.786,0.303)]
$

4. Regarding the cost attribute, the levels always appear in pairs (e.g., 50, 100, 150, 200). If I were to use dummy coding for cost, should I still assign non-zero priors to each dummy level in the Bayesian design (e.g., D.dummy[-0.0001|-0.0002|-0.0003]*D[1,2,3,0])?
More broadly, I am a bit uncertain about which model specification to use during estimation, and whether the coding scheme used in the design phase (e.g., dummy vs. effects coding) must align with the estimation model I will later apply. Any clarification would be extremely helpful.
Model 1
------------------------------------------------------------------------------
choice | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
A | .8817433 .0871399 10.12 0.000 .7109523 1.052534
B | .4317625 .0778809 5.54 0.000 .2791187 .5844062
C | .2462197 .0752601 3.27 0.001 .0987126 .3937268
D | .5230531 .0759689 6.89 0.000 .3741568 .6719495
E | -.0024924 .0011808 -2.11 0.035 -.0048068 -.000178
ASC | .2186871 .2420185 0.90 0.366 -.2556605 .6930347
Model 2
------------------------------------------------------------------------------
choice | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
A1 | 1.439732 .1767985 8.14 0.000 1.093213 1.786251
A2 | 2.01239 .1945652 10.34 0.000 1.63105 2.393731
B1 | .9119698 .1819027 5.01 0.000 .555447 1.268493
B2 | .8899421 .1584458 5.62 0.000 .579394 1.20049
C1 | .4338534 .1690078 2.57 0.010 .1026042 .7651026
C2 | .7543231 .1691705 4.46 0.000 .422755 1.085891
D1 | .111323 .1639312 0.68 0.497 -.2099764 .4326223
D2 | .9814282 .1540309 6.37 0.000 .6795332 1.283323
E | -.0020909 .001212 -1.73 0.084 -.0044664 .0002846
ASC | .7858964 .3030364 2.59 0.010 .1919561 1.379837
------------------------------------------------------------------------------
Many thanks!
Best wishes,
Ethannn

[Michiel Bliemer]

See my response in the other topic.