Dear choice modellers,

I have finished to collect the pilot data of my DCE with a sample of 24 respondents. I have therefore estimated a conditional logit (with the Nlogit software) in order to use the priors for my efficient design in Ngene for the final data collection. Here are the results I get:

Discrete choice (multinomial logit) model

Dependent variable Choice

Log likelihood function -131.77138

Estimation based on N = 144, K = 10

Inf.Cr.AIC = 283.5 AIC/N = 1.969

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Log likelihood R-sqrd R2Adj

Constants only -142.6627 .0763 .0431

Note: R-sqrd = 1 - logL/Logl(constants)

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Chi-squared[ 8] = 21.78269

Prob [ chi squared > value ] = .00533

Response data are given as ind. choices

Number of obs.= 144, skipped 0 obs

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| Standard Prob. 95% Confidence

CHOICE| Coefficient Error z |z|>Z* Interval

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SUPPINDI| .25548 .30245 .84 .3983 -.33732 .84828

SUPPCOL| .44822 .28504 1.57 .1158 -.11044 1.00689

TVXINDGP| .07871 .32121 .25 .8064 -.55086 .70827

TVXCOOP| -.01138 .33274 -.03 .9727 -.66353 .64078

COST|-.33857D-04 .2752D-04 -1.23 .2186 -.87794D-04 .20079D-04

PULVINGP| -.86147*** .28159 -3.06 .0022 -1.41339 -.30956

PULVCOOP| -.73240** .30344 -2.41 .0158 -1.32713 -.13767

TECHCOLL| .43551* .22877 1.90 .0570 -.01288 .88389

A_ALT1| 1.42733*** .45573 3.13 .0017 .53412 2.32054

A_ALT2| 1.37941*** .47549 2.90 .0037 .44746 2.31136

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nnnnn.D-xx or D+xx => multiply by 10 to -xx or +xx.

***, **, * ==> Significance at 1%, 5%, 10% level.

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1. My two first attributes are not statistically significant: does it necessarily mean that I need to exlcude them from my model for the collection of the final data? In my opinion, my sample if too small to draw strong conclusions at that stage so I would prefer to keep at least the COST and the SUPP attributes as they are almost significant. Furthermore, I would be very limited later on for the analysis if I drop my monetary/cost attribute. If I keep these attributes, should I however increase the amount of levels for the COST attribute? The reason for the non significance may be the lack of variation.

2. Would you recommend me to estimate other models than a conditional logit at that stage or it is fine to extract my priors based on the results of the estimation of a conditional logit?

3. What other steps do you recommend me before extracting my priors?

Based on the results above my attributes and levels are:

Support type 1 attribute --> 3 levels (none,SUPPINDI= personalized, SUPPCOL=collective), dummy variable

Mode of plantation attribute --> 3 levels (individual, TVXINDGP= collective form1, TVXCOOP=collective form2), dummy variable

Cost (COST) attribute --> 4 levels (45 000 €,50000 €,55000 €,60000€), continious variable

Mode of spraying --> 3 levels (individual, PULVINDGP=collective form1, PULVCOOP=collective form2), dummy variable

Support type 2 attribute --> 2 levels (personalized, TECHCOLL=collective), dummy variable

Thanks a lot in advance for your advice!

Best,

Gaëlle