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?
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.
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?
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!!
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?
I am setting-up a mover-stayer 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 mis-classified as movers?
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 problem-free responding is a necessary feature of mover-stayer 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 two-part 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 mover-stayer 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 5-class model, would it then be 2, 1, 0, -1, -2? Thank you.