Thanks for the Mplus V7.1 which has a lot of new and useful features. I have a question related to covariate testing in mixture model (factor mixture model).
Originally I used DU3STEP/DE3STEP to analyze the covariates. However, the new V7.1 gave warning messages for a lot of my auxiliary variables, namely, that the classification error between Step 1 and Step 3 exceeded 20%. Apparently there were no results for those variables.
I understand that this check is a new feature for V7.1. In this case, should I use the new DCON/DCAT for distal outcomes? This new approach did not give this warning message (except for listwise deletion).
It is relieving that I can go for the DCON/DCAT auxiliary approach in analyzing the distal outcomes, because I do not know how to deal with the many error warnings associated with the DU3STEP/DE3STEP approach.
Thanks for your help. I have a follow up question regarding the dcon/dcat. I am interested in using this analysis, but have Mplus7, not Mplus7.1. Is there a manual code that I can use to run the same analysis?
Laina Isler posted on Wednesday, June 12, 2013 - 2:38 pm
Thanks, I will look into updating it.
I am getting an entropy of 0.611, which I understand is not strong at all, but my average latent class probabilities for most likely class membership range from 0.815 to 0.74. And the classes obtain good chi-square values in the 3 step and 1 step equality tests.
I am not sure whether I should interpret the data as demonstrating distinctive classes, or whether the entropy is too low. I have tried changing number of classes, and this does not improve entropy. Is there another way to improve entropy?
Laina Isler posted on Wednesday, June 12, 2013 - 3:12 pm
Also - I am dealing with data sets of 4500 to 6900. I am not certain whether entropy may be negatively effected by class size.
An entropy of 0.6 is ok. You should not decide on the number of classes based primarily on entropy - use BIC instead. Sample size does not have to do with entropy.
Laina Isler posted on Wednesday, June 12, 2013 - 4:15 pm
Thanks for the quick reply. The BIC are very large, so I am not certain how much of a difference is necessary to support one number of classes over another. Furthermore, the BIC keeps decreasing when I add classes long after further tests (Vuong-lo-mendell-runin likelihood ratio test) indicate that fewer classes would be sufficiant.
For instance, a BIC of 322937.566 vs. 322838.965 vs. 322769.701 were obtained for 4, 5, 6 classes, respectively. While the tech11 output (lo-menell-ruban adjusted LRT) is 0.008, 0.0102, and 0.0922, respectively.
Does this indicate the a 5 class solution is attained, despite BIC values continuing to decrease, (up to 9 classes continue to show a decrease in BIC)
Yes, Tech11 gives support to 5 classes, but given that BIC continues to decline, the conclusion isn't clear. I would recommend what we teach in Topic 5, namely to look at how different the solutions are for 5 and higher number classes. Look at the profile plots - perhaps 6 and more classes are merely uninteresting variations on 5-class themes.
Thanks for your help. The 5 class seems the most reasonable. However, I am getting altered models (class size, average latent class probabilities and plot) depending on whether I run the data with syntax to test covariates (du3step), or likelihood ratio tests (tech11 and tech14).
I thought these tests were not supposed to effect the classes themselves, and was wondering which output would be more accurate, or whether there is a way to run the tests wihtout impactin class results?