

A question about crosssectional LCA 

Message/Author 

Jon Heron posted on Tuesday, February 27, 2007  12:00 pm



Sorry for all the questions today! I'm working on a crosssectional 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. Completecase results are in support of a nice 3class 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 completecase analysis but ignoring the partialmissing stuff seems a bit posthoc. Any suggestions? many thanks Jon 


It sounds like perhaps the threeclass solution was not very stable. Perhaps you should look at the original fourclass solution. 

Jon Heron posted on Wednesday, February 28, 2007  8:33 am



The 4class solution is very similar between the completecase and partialmissing models. 4 classes: ~6300, 400, 50 and 10 cases So when moving to 3class, the completecase model drops the smallest group whereas the partialdata model drops the group of 50. I guess you're right that it would be simpler to discuss the 4class 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  7:13 pm



Hi, I am working on a crosssectional 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: MODEL RESULTS TwoTailed Estimate S.E. Est./S.E. PValue Latent Class 1 Means DEPR#1 2.399 0.221 10.836 0.000 DEPR#2 2.058 0.190 10.846 0.000 EMPL#1 0.344 0.244 1.409 0.159 EMPL#2 1.676 0.254 6.605 0.000 GAF#1 1.169 0.152 7.700 0.000 GAF#2 3.759 0.587 6.409 0.000 Thank you very much. 


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 revalued 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? Thanks again. 


Yes. See the CATEGORICAL option for a description of how the data are recoded. 

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