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 Anonymous posted on Tuesday, April 30, 2002 - 1:41 pm
I had three GMM models:
Model 1: a simple GMM without predictors of class membership and growth factors.
Model 2: predictors of class membership were included.
Model 3: both predictors of class membership and growth factors were included

Model 2 had exactly the same class classification as Model 1. However, the class sizes changed somewhat in Model 3 when predictors of growth factors were included.
Can I report class classification based on the results of Model 1, and interpret the impact of covariates on growth factors based on the results of Model 3?

Thank you very much for your help.
 Bengt O. Muthen posted on Tuesday, April 30, 2002 - 2:36 pm
No, use Model 3 for classification as well if that is your final model. If you are concerned about the change in classification in Model 3, try to modify Model 3. Perhaps some of the covariates have a direct influence on some of the outcomes. Also, you may want to study the individuals who change class membership to understand why that happens.
 Anonymous posted on Wednesday, May 01, 2002 - 10:04 am
Dear Dr. Muthén:
Thank you so much for your quick answers to my questions. They are very helpful. I wish I could have one more question.
Once the predictors of growth factors are included in the model, Mplus does not print out estimated mean values and S.E.s of the growth factors in default. So, option TECH4 is used to print the mean values and estimated variances of the growth factors. To my understanding, the estimated variances of growth factors measure the variation of the random coefficients, and we can use their square roots for significance tests. However, I found that the square roots of the variances of growth factor were much larger than expected. Then, I removed the predictors of growth factors and ran the model again with option TECH4 . I found that the square roots of the estimated variances of the growth factors were much larger than the S.E.s of the growth factors that Mplus provides.
I would like to know how I could test the significance of the growth factors when predictors of the growth factors are included in the model. Thank you very much for your help.
 Bengt O. Muthen posted on Thursday, May 02, 2002 - 7:20 am
I believe that you are confusing the standard error of the parameter estimate and the variance/standard deviation of a growth factor. When covariates are included in the model, a residual variance of a growth factor is estimated rather than the variance of a growth factor. This means that with covariates, significance testing focuses on the residual variance and the slopes for the covariates because this is how the model decomposes the variance of the growth factor. You typically don't test for the growth factor variance being zero. The significance test for any parameter in the model is found in column three of the results, the ratio of the parameter estimate to its standard error.
 Anonymous posted on Friday, May 03, 2002 - 7:23 am
You are right. S.E.s refer to sampling variation in parameter estimates, and variances of random coefficients measure variation of the coefficients across cases. Thanks for pointing out that the variance of growth factor is decomposed into two parts (explained and unexplained) when covariates are included.
 Harald Gerber posted on Saturday, October 04, 2008 - 3:30 am
Sorry, for that somewhat simple question, but how can one compute a significance test of the means of the growth factors given in tech 4 (conditional model)? I'm not sure if the question above deals with the same issue (it is more variance testing of growth facors?). But I want to test growth factor means in conditional models not growth factor (residual-)variances.
Thank you for your time!
 Linda K. Muthen posted on Saturday, October 04, 2008 - 11:57 am
You can use MODEL CONSTRAINT to define the mean or run the unconditional model.
 emm plaza posted on Monday, June 22, 2009 - 1:43 pm
Hi,

I have a question about minimum number of subjects:

My latent class analyses are based on two variables in two different samples with 1400 and 1000 subjects respectively. My problem is that when I assess model fit, the number of classes suggested gives some classes that are very small (between 7 and 26 subjects in some classes). My worry is that the classes, although they have model fit and also are theoretically meaningful, might be too small. Could this be the case? Is there a minimum limit of subjects in each class in order for it to be meaningful?

Thanks!
 Linda K. Muthen posted on Monday, June 22, 2009 - 5:31 pm
It's hard to know. I know of no minimum limits.
 Arne Floh posted on Tuesday, November 17, 2009 - 6:19 am
Linda,

how can I calculate the BLRT mentioned in Nyland/Asparouhov/Muthen (2007) to determine the optimal number of classes?

It should be implemented in Mplus since the 4.1 release.

Thx,

Arne
 Linda K. Muthen posted on Tuesday, November 17, 2009 - 9:19 am
TECH14 of the OUTPUT command give BLRT. It cannot be calculated by hand.
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