Auxiliary variables in 3-step LTA PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
 Feihong Wang posted on Thursday, February 19, 2015 - 11:37 am
Dear Dr. Muthen,
if I completed the 3-step LTA with one set of auxiliary variables, if I want to use a subset of those variables in step 3, do I need to complete step 1 and step 2 again with that subset. My understanding is that the auxiliary variables are included in step 1 and 2 so that they are kept in the new dataset for step 3. Therefore, I donít think I have to do the steps over. Correct?
 Bengt O. Muthen posted on Thursday, February 19, 2015 - 2:20 pm
You don't need to complete step 1 or 2 over again. Those steps do not include the auxiliary variables.
 Feihong Wang posted on Wednesday, March 18, 2015 - 12:00 pm
Hi, Dr. Muthen,
we are working on LTA 3-step procedure. We have used both parameterization 1 and parameterization 2. Is there a way with either or both parameterization to fix a latent transition probability based on the estimated model to zero. We have 4 latent groups at two time points, and would like to fix the latent transition probability to zero from group 1 at time 1 to group 2 at time 2.
 Bengt O. Muthen posted on Wednesday, March 18, 2015 - 12:06 pm
In Step 3 you do c2 on c1 and that's where you can fix a transition probability to zero - either by fixing a logit slope to -15 or by switching to parameterization= probability; see UG and the V7Part2 handout for the 2012 August Utrecht short course.
 Feihong Wang posted on Thursday, March 26, 2015 - 11:28 am
Dear Dr. Muthen,
I have completed the 3-step procedure. The sample size at step 1 is 1200. The sample size at step 2.1 is smaller (n=1156) than the sample size at step 1, and the sample size at step 2.2 is smaller than the sample size at step 2.1 (N=972). Is using the 3-step procedure when there are cases with missing data recommended, or would it be preferable to use the sample with N=972 in all three steps? Or do you have an alternate better recommendation?
 Tihomir Asparouhov posted on Thursday, March 26, 2015 - 3:11 pm
It sounds like the latent class measurement model for some observations is available only at one of the two time points and is missing at the other. I would insert missing values for the nominal class indicator when that happens (when the measurement model is missing entirely at certain time points) - that way you will be able to keep the entire sample size. Make sure you monitor the data carefully so you can see which observations are dropped in Mplus during the different stages.

Alternatively you can actually keep all observations by adding a dependent but uncorrelated variable to each of the steps that has no missing values (that way the data files will stay the same size). Make sure the new variable does not affect the latent class measurement model.

The first method is probably better.
 Feihong Wang posted on Thursday, September 22, 2016 - 7:47 pm
Dear Dr. Muthen,
I have conducted a latent transition analysis using variables with 7 categories. I have used the results in the probability scale to calculate model-implied means of the observed variable. That is, for each observed variable, I summed the products of the response scores (1 to 7) and the probabilities of these scores. I would like to know if standard errors of these means can be correctly calculated by summing products of the sampling variances for the probabilities and the probabilities of the scores. Or does sampling dependence among the probabilities cause the calculation to be incorrect? I would also like to know if model-implied means for the observed variables are independent within each latent class and across latent classes. If you have a better idea for comparing model-implied means, I would like to hear it.
Thank you for your help.
 Bengt O. Muthen posted on Friday, September 23, 2016 - 6:32 pm
It sounds like you declare your variables as categorical which means they are treated as ordinal. But when you create means you have to give scores to the categories which then force them to be equidistant which the model does not assume. So you lose the advantage of the ordinal modeling.

With 7 categories and without strong floor or ceiling effects I would simply treat them as continuous and avoid the complications.

Treating them as ordinal, I would focus on the probabilities of each category and collections of categories (such as the top 2 , top 3, etc).
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