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I am trying to determine the best fit # of classes for an LPA/LCA with continuous variables, and I'm running into some problems I haven't seen before. I'm using 3 variables and the sample size is about 4,300. Here's the basic info: a. All the IC statistics continue improving as I add more classesI've run up to 8 classes so far. b. The parametric boot strap test also improves with each additional group, although I get errors that the best LL was not replicated in all draws. c. The VLMR and the LMR suggest that a 6class model fits best. d. For all classes >4, the loglikelihood is only replicated a small number of times. For example, I ran the 6class model with 10,000 starts, and the best LL was replicated only 3 times, suggesting a local maximum. One thing I'm noticing is that is some classes from models with 5 or more classes have either a mean or a SE on one of the indicators of 0. As the three indicators are counts, a substantial portion of the sample has a value of 0 for one or more indicator (26%38%). Does it indicate a fundamentally unusable/unidentified model? Thanks in advance for any help anyone can provide. 


Best *fit*, sorry. 


Perhaps you should do your LCA treating the variables as counts instead. When these mixture fit indices don't show an optimum this can be a sign that the type of model isn't suited for the data. For instance a factor mixture model may be more suitable. 

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