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 Jeffrey M Girard posted on Monday, June 29, 2015 - 11:50 am
I have data for participants from 12 different countries. As this number is too small for TYPE=COMPLEX or TYPE=TWOLEVEL analyses, I have been trying to account for the clustering using dummy variables.

ANALYSIS:
TYPE = GENERAL;
MODEL:
y ON x1
x2
x3
x1byx2
x1byx3
x2byx3
d1-d11;

However, when I add the 11 dummy variables to my model, as above, I get the following warnings/errors:

WARNING: THE SAMPLE COVARIANCE OF THE INDEPENDENT VARIABLES IS SINGULAR. PROBLEM INVOLVING VARIABLE D11.

THE MODEL ESTIMATION TERMINATED NORMALLY

THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING THE FOLLOWING PARAMETER:
Parameter 18, Y ON D11

Thanks in advance.
 Bengt O. Muthen posted on Monday, June 29, 2015 - 5:26 pm
Here are a couple of things you can do to try to figure out why you have this problem in the data.

1. Think about why you might have an x variable that is a linear combination of other x variables.

2. Delete the d1-d11 vbles to see if you get the problem in your regression run. If it doesn't happen, add 1 dummy at a time to see when the problem happens.

3. do a type=basic run and inspect the correlation matrix.
 Jeffrey M Girard posted on Tuesday, June 30, 2015 - 8:36 am
That is very helpful, thank you.

Thinking more about your point #1, it makes sense that any cluster-level predictors will be collinear with the dummy variables.

In this example, x3 was a country-level variable and this was what was causing problems.

I'm afraid the answer is no, but is there any way (without COMPLEX or TWOLEVEL) to include cluster-level predictors when accounting for clustering with dummy variables?
 Bengt O. Muthen posted on Wednesday, July 01, 2015 - 5:19 pm
You can do that without complex or twolevel.
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