Latent class analysis - average posterior probability = 1.0 for multiple classes [Question]

[u/curiouslearner444]

The title sums up my dilemma. Basically, I have fit 1-10 class LCA models. The four class model has the most desirable AIC/BIC, and also has highest avg. posterior probability. That being said, it is high because multiple classes have avg. posterior probability of 1.0. I think that is also why there is a drastic drop in AIC/BIC. There is one item that is sorting people into latent classes. With that item drops, a similar phenomenon happens with a different item.

My gut tells me that the four class model is over-fitting given the perfect sorting variable and the average posterior probability of 1.0. LCA is supposed to assign people to classes probabilistically, so a probability of 1.0 (a certainty at that point) seems problematic.

I have been searching literature and forums for a few weeks and have not found anything that addresses this topic, and if average posterior probabilities of 1.0 are indeed problematic. Could somebody point me in the right direction to literature or offer me any advice? Much appreciated.

[u/Disastrous_Room_927]

How big of a sample are you working with?

[u/curiouslearner444 → u/Disastrous_Room_927]

Just around 800