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dear Linda and Bengt, I am working on a LTA and trying to include a mover/stayer variable in the model. I am using the following paper as examples: Mplus User Guide (exp 8.14), Nylund 's dissertation (2007), and Kpaln (2006), which are all in your webpage. I am a bit confuse bacause of the different threshold values used in these examples. Nylund uses 15 for the intercepts and 30 and 45 for the "b" coefficients. Muthen and Kaplan use 10 and 10 for the intercepts and 20 for the "b" coefficients. I am modeling a 3 time points LTA with the following number of classes: CLASSES c(2) c1(4) c2(4) c3(4) could you please help me to understand the meaning of these threshold values in order to be able to apply them to my model? thanks a lot in advance, luca 


The values of a and b should be selected such that the sum is a large value, for example, if a is 15 b should be 30 so that the sum is 15. If a is 15 b should be 30 so that the sum is 15. The sum is used as the logit value determining the probability of transitioning. 


thanks Linda, thus, a sum (a+b) equal to 15 represent a probability of 1, whereas a sum equal to +15 represent a probability of 0. Is it correct? Then, if this is the case, what is the menaing of values larger than +15? Has +10 the same meaning as +15? Furthermore, represent +3 very low and very high probabilities? thanks again luca 


Yes. Plus or minus 10 or plus or minus 15 doesn't matter. It just needs to be a large value. Plus or minus 3 may not be large enough. 


Thanks Linda, It seems to work when I claculate a LTA for two time points. In this case if I check the "most likely latent class pattern" I see clearly that the "stayer" class reports values only for the "stayer" patterns, i.e. 2111, 2222, 2333, 2444. The rest is zero. The corrsponding patterns for the "mover" class are close to zero. Is it a clue that my model is corectly specified??? However, when i calculate a model with three time points the "stayer" patterns are all zero but for the 2111 pattern. Furthermore, the same patterns for the "mover" class (i.e. 1111,1222,1333,1444) have in this case relatively large values. Shouldn't these individuals be classified in the "stayer" class??? I really don't know how to interpret these results!! thanks a lot in advance luca 


Please send your output and license number to support@statmodel.com. 


I am running LTA following Nylund et al. exactly. I have 3 timepoints and 3 classes made from two continuous variables. I fixed my intercepts at 15 for the stayers as well as the b coefficients at 30 and 45 just as in Nylund et al. However, I keep getting this message: ONE OR MORE MULTINOMIAL LOGIT PARAMETERS WERE FIXED TO AVOID SINGULARITY OF THE INFORMATION MATRIX. THE SINGULARITY IS MOST LIKELY BECAUSE THE MODEL IS NOT IDENTIFIED, OR BECAUSE OF EMPTY CELLS IN THE JOINT DISTRIBUTION OF THE CATEGORICAL LATENT VARIABLES AND ANY INDEPENDENT VARIABLES. THE FOLLOWING PARAMETERS WERE FIXED: 20 22 26 15 21 Do I need to fix the parameters for the movers as well b/c perhaps there is not much movement at least between time 1 and time 2? 


Can't be diagnosed without seeing the context  please send your input, output, data and license number to support@statmodel.com. 


I am settingup a moverstayer model using the Nylund dissertation example from appendix H as a guide. I have 2 timepoints, 3 classes per wave. A problem is recurring where I get a subset of movers (c#1) who are actually stayers in the reference condition. Since you can't include references to the slope of the reference class, is there a way to reduce/eliminate the possibility of stayers being misclassified as movers? 


Please send your output and license number to support@statmodel.com. 


I would like to run example 8.14 with continuous outcome variables. What should I replace the logit tresholds (e.g., @15) with? 


The c on c logits aren't affected by the type of outcome, but perhaps you refer to the u logits. In ex8.14 they are used to specify that "the stayers represent individuals who do not exhibit problem behaviors." I don't know that this tying together stayers with problemfree responding is a necessary feature of moverstayer modeling or could be eliminated (see the references we give). I don't know how to handle that feature with continuous outcomes unless you use a twopart model for the outcomes and specify that these individuals are in the zero portion. 

Julia Lee posted on Thursday, April 12, 2012  2:34 pm



Hi Linda, I wrote to you about my LTA moverstayer question quite some time back. You provided an excellent explanation about OVERALL and MODEL C. However, I am still not clear about the interpretation of the syntax below: 1. Would you kindly explain what is the interpretation of the model specific (i.e., MODEL C.C1 at time 1) thresholds for the stayer group based on this syntax below? 2. If it is a 5class model, would it then be 2, 1, 0, 1, 2? Thank you. MODEL C.C1: %C#1.C1#1% [x11x15] (15); %C#1.C1#2% [x11x15] (610); %C#1.C1#3% [x11x15] (1115); %C#1.C1#4% [x11x15] (1620); %c#2.c1#1% [x11x15*2] (2125); %c#2.c1#2% [x11x15*1] (2630); %c#2.c1#3% [x11x15*1] (3135); %c#2.c1#4% [x11x15*2] (3640) 


See the following FAQ which is on the website: LTA with MoversStayers 

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