Variable omitted because of collinearity- clogit

[Fathallah Kerouaz]

Dear All,

I have data from a choice experiment with 840 individuals who answered from 6 sets of situations each. Each set of the situation requires from individuals to choose between two options and "SQ " situation. I have are five attributes and the qualitative variables have been dummy-coded. An attribute included in my choice experiment ‘Uncertainty’ is encoded using three dummy-coded variables (“RedUncertainty” and “IncrUncertainty” and "Inchanged" for the SQ situation). When I ran the clogit model, the Reduced Uncertainty was omitted because of multicolinaity ?

How can I solve this problem?

[Andrew Musau]

An attribute included in my choice experiment ‘Uncertainty’ is encoded using three dummy-coded variables (“RedUncertainty” and “IncrUncertainty” and "Inchanged" for the SQ situation)

Why not use factor variables and enter these indicators as

i.Uncertainty

?

Same for other indicators. In this way, you can rule out coding errors in generating the indicators. See

help fvvarlist

Missing values in other variables may lead to either "RedUncertainty" or "Inchanged" being dropped due to listwise deletion, leaving only 2 categories. This becomes apparent when using factor variable notation, as one of these categories would display as "empty".

[Fathallah Kerouaz]

Thanks for your reply,

I used this coding for the ‘Uncertainty’ variable because in the context of the choice experiment method, we use ‘Dummy coding or effects coding’ as noted in the literature:
ttps://www.sciencedirect.com/science/article/pii/S1755534516300781#fn4
https://onlinelibrary.wiley.com/doi/...0.1002/hec.984

But I noticed that in my 6 choice cards, the ‘Unchanged’ level does not appear in either of the two alternative situations to the status quo situation. The ‘RedIncertainty’ level takes the value 1 when it exists in the choice cards and 0 if the "IncrUncertainty" level appear.

Normally, in dummy coding, the "RedIncertainty" level takes the value 1 when it exists in the choice cards and 0 if the ‘IncrUncertainty’ or ‘Unchanged’ levels appear in the choice to be made. But, in my case, the "Uchanged" level never appears in my 6 choice cards, so I end up with a situation where the "RedIncertainty" level takes the value 1 when it exists in the choice card and 0 if the "IncrUncertainty" level is present. And the "IncrUncertainty" level takes the value 1 when it exists in the choice card and 0 if the "RedUncertainty" level is present.

These levels test how individuals react to increased uncertainty (IncUcertaintry) and reduced uncertainty (RedUncertainty) compared to the status quo situation where uncertainty is unchanged (Unchanged).


Thank you for any help.

[Andrew Musau]

The point is that factor variables will create the indicators for you, instead of doing this yourself.

Normally, in dummy coding, the "RedIncertainty" level takes the value 1 when it exists in the choice cards and 0 if the ‘IncrUncertainty’ or ‘Unchanged’ levels appear in the choice to be made. But, in my case, the "Uchanged" level never appears in my 6 choice cards, so I end up with a situation where the "RedIncertainty" level takes the value 1 when it exists in the choice card and 0 if the "IncrUncertainty" level is present. And the "IncrUncertainty" level takes the value 1 when it exists in the choice card and 0 if the "RedUncertainty" level is present.

You have answered your own question then with this description. There is no "Unchanged" category in your dataset, so with "IncrUncertainty" in the regression, "RedIncertainty" is the reference (base) level. In other words, you have a binary variable "Uncertainty", as opposed to a variable with 3 levels.

[Fathallah Kerouaz]

That's true and I agree with you. But the problem is that in my design, the base level is "unchanged". Respondents compare the other uncertainty levels "RedUncerainty" and "IncrUncertainty" with the "Unchanged" level of the status quo situation. So, with this collinearity problem, I don't know how to justify or interpret this?

[Andrew Musau]

How was (were) the question(s) framed and can you show us a sample of the responses? Use dataex to do so.

[Fathallah Kerouaz]

The questions are 6 choice cards where respondents are asked to select their preferred options given the benefits/costs that each represents.
The ‘Unchanged’ level never appeared for the ‘Uncertainty’ attribute, unlike the other attributes.
Here is an example.

I have copied the data using XXX . I hope I've used it correctly.

dataex choiceeff ascSQ NeighbAdoptbis IncrUncertaintybis RedUncertaintybis GreatBioRegbis SlightBioRegbis GreatEnviQualitybis SlightEnviQualitybis AnnValuebis

----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. For more info, type help dataex
clear
input float(choiceeff ascSQ NeighbAdoptbis IncrUncertaintybis RedUncertaintybis GreatBioRegbis SlightBioRegbis GreatEnviQualitybis SlightEnviQualitybis AnnValuebis)
0 0 1 0 1 0 0 0 1 550
1 0 0 1 0 1 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 0 0 1 1 0 1 0 250
1 0 1 1 0 0 0 0 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 0 0   0
1 0 1 0 1 0 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 1 0 1 1 0 0 0 550
1 0 1 1 0 0 0 1 0   0
0 1 0 0 0 0 0 0 0   0
1 0 0 0 1 0 0 1 0 250
0 0 1 1 0 1 0 0 0   0
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1 0 0 1 0 1 0 1 0 550
0 0 1 0 1 0 0 0 0 550
0 1 0 0 0 0 0 0 0   0
1 0 1 0 1 0 0 0 1 550
0 0 0 1 0 1 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 0 0 1 1 0 1 0 250
1 0 1 1 0 0 0 0 0   0
0 1 0 0 0 0 0 0 0   0
1 0 0 1 0 1 0 0 0   0
0 0 1 0 1 0 0 1 0 550
0 1 0 0 0 0 0 0 0   0
1 0 1 0 1 1 0 0 0 550
0 0 1 1 0 0 0 1 0   0
0 1 0 0 0 0 0 0 0   0
1 0 0 0 1 0 0 1 0 250
0 0 1 1 0 1 0 0 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 1 0 550
1 0 1 0 1 0 0 0 0 550
0 1 0 0 0 0 0 0 0   0
0 0 1 0 1 0 0 0 1 550
1 0 0 1 0 1 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 0 0 1 1 0 1 0 250
1 0 1 1 0 0 0 0 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 0 0   0
1 0 1 0 1 0 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 1 0 1 1 0 0 0 550
1 0 1 1 0 0 0 1 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 0 1 0 0 1 0 250
1 0 1 1 0 1 0 0 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 1 0 550
1 0 1 0 1 0 0 0 0 550
0 1 0 0 0 0 0 0 0   0
0 0 1 0 1 0 0 0 1 550
0 0 0 1 0 1 0 1 0 550
1 1 0 0 0 0 0 0 0   0
0 0 0 0 1 1 0 1 0 250
0 0 1 1 0 0 0 0 0   0
1 1 0 0 0 0 0 0 0   0
1 0 0 1 0 1 0 0 0   0
0 0 1 0 1 0 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 1 0 1 1 0 0 0 550
0 0 1 1 0 0 0 1 0   0
1 1 0 0 0 0 0 0 0   0
0 0 0 0 1 0 0 1 0 250
1 0 1 1 0 1 0 0 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 1 0 550
0 0 1 0 1 0 0 0 0 550
1 1 0 0 0 0 0 0 0   0
1 0 1 0 1 0 0 0 1 550
0 0 0 1 0 1 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 0 0 1 1 0 1 0 250
0 0 1 1 0 0 0 0 0   0
1 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 0 0   0
0 0 1 0 1 0 0 1 0 550
1 1 0 0 0 0 0 0 0   0
0 0 1 0 1 1 0 0 0 550
0 0 1 1 0 0 0 1 0   0
1 1 0 0 0 0 0 0 0   0
0 0 0 0 1 0 0 1 0 250
0 0 1 1 0 1 0 0 0   0
1 1 0 0 0 0 0 0 0   0
1 0 0 1 0 1 0 1 0 550
0 0 1 0 1 0 0 0 0 550
0 1 0 0 0 0 0 0 0   0
1 0 1 0 1 0 0 0 1 550
0 0 0 1 0 1 0 1 0 550
0 1 0 0 0 0 0 0 0   0
1 0 0 0 1 1 0 1 0 250
0 0 1 1 0 0 0 0 0   0
0 1 0 0 0 0 0 0 0   0
0 0 0 1 0 1 0 0 0   0
1 0 1 0 1 0 0 1 0 550
0 1 0 0 0 0 0 0 0   0
0 0 1 0 1 1 0 0 0 550
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 11466 observations
Use the count() option to list more

[Andrew Musau]

If the choice set consists of two options and you allow respondents not to choose, then those who choose not to choose given the options "increased" and "reduced" can be assumed to have chosen "no change". However, some respondents may choose not to choose because they do not want to reveal their preference. In this case, you are unable to disentangle them from those whose preference is no change. If you are focusing on positive changes or increases, you can specify responses as binary:

Greatly Improved - \(\neg\) Greatly Improved = Same level or worse
Increased - \(\neg\) Increased = Same level or decreased

In this way, the ambiguity disappears.

[Fathallah Kerouaz]

Thank you very much for your answers, they're very helpful!

If I've well understood, I can only include the (Reduced Uncertainty) level in my model by assuming that the respondents choosing this option are avoiding the current level (Unchanged) or worse (Increased Uncertainty) level of uncertainty. Of course, this is assuming that all respondents choosing the option reveal their preferences.

[Andrew Musau]

Correct.