Jon Heron posted on Tuesday, February 27, 2007 - 6:00 am
Sorry for all the questions today!
I'm working on a cross-sectional LCA model of 12 binary symptoms thought to be precursors of schizophrenia.
Complete case dataset consists of 6,744 kids. There are another 47 kids with 1 missing response.
Complete-case results are in support of a nice 3-class model with class sizes of approx 6351, 347 and 47 cases.
Incorporating the remaining 47 cases with a single missing value, either by recoding missing to no, or with type = missing option, causes a different third class to pop up in place of the small one mentioned above. This class only has 9 cases and, most strangely, only one of these 9 is a child with partial missingness.
I'm not sure what to do now. I like the results obtained from the complete-case analysis but ignoring the partial-missing stuff seems a bit post-hoc.
It sounds like perhaps the three-class solution was not very stable. Perhaps you should look at the original four-class solution.
Jon Heron posted on Wednesday, February 28, 2007 - 2:33 am
The 4-class solution is very similar between the complete-case and partial-missing models.
4 classes: ~6300, 400, 50 and 10 cases
So when moving to 3-class, the complete-case model drops the smallest group whereas the partial-data model drops the group of 50.
I guess you're right that it would be simpler to discuss the 4-class as there is more consistency, but I'm not really happy with presenting a model where there's a group of 10 kids.
Qilong Yuan posted on Wednesday, June 23, 2010 - 1:13 pm
Hi, I am working on a cross-sectional LCA model. I have 8 nominal class indicators and am trying to get the class membership for the subjects. The class indicators are class memberships themselves and have values like 1, 2, and 3. I am wondering what the estimates under model results in the output mean. Below is part of it:
The parameter estimates under Means are intercepts in a multinomial logistic regression where all covariates are zero. See Calculating Probabilities From Logisitic Regression Coefficients in Chapter 14 of the Version 6 Mplus User's Guide and Chapter 13 of earlier guides to see how these values are used.
Hi Linda, thanks for your reply. This is very helpful.
I have another question. I noticed that my original variables (having values 1, 2, 3, and 4) were re-valued as 0, 1, 2, and 3. Is it correct that the values were matched this way: 1->0, 2->1, 3->2, and 4->3?