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 Marike Deutz posted on Thursday, March 28, 2013 - 6:18 am
Dear Bengt and Linda Múthen,

I am conducting a multi-group CFA (2 groups: gender) and I get the following error messages:

*** ERROR
Group 1 does not contain all values of categorical variable: ITEM14
*** ERROR
Group 1 does not contain all values of categorical variable: ITEM31
*** ERROR
Group 1 does not contain all values of categorical variable: ITEM1

Both groups have scores on the two categories (0 and 1) for the three items mentioned in the error messages. 100% sure.

However, for girls there are also missings on these variables (1 for each item) while there are no missings for boys on these items. So it seems this should be the problem. However, I did indicate Missing are all (99).

How can I make the program work so I don't have to delete my 3 cases with only 1 missing score on an item? So how can the program work with missings on one group but not on the other?


Thank you!
 Linda K. Muthen posted on Thursday, March 28, 2013 - 7:25 am
It sounds like you are reading the data incorrectly. Please send the output, data, and your license number to support@statmodel.com.
 JackBox posted on Thursday, May 08, 2014 - 12:54 am
Hi. I have got the same error message when trying to do a configural invariance analyse with ordinal variables.

I was following an example (http://people.ku.edu/~s044p855/catInvariance.html), which also seems to have some category options not used. This example does not give error messages.

But I do have some missing data (-999) as the previous writer. Is the handling of the missig data the cause of this problem?

What was the solution for the first writer's problem?
 Bengt O. Muthen posted on Thursday, May 08, 2014 - 6:14 am
If you don't have the same number of categories in the data for all groups, you should use the * approach. See the UG page 544.
 Linda K. Muthen posted on Thursday, May 08, 2014 - 7:01 am
You would need to send your files and license number to support@statmodel.com for us to diagnose your error message.
 JackBox posted on Sunday, May 11, 2014 - 11:10 pm
The * approach seems to solve the category problem.

Thank you very much.
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