Hi, Apologies if this is a too simple question, or previously dealt with, but I could not find it in previous threads.
I am running a LCA model to identify subgroups of patients based on a number of symptoms. The variables for symptoms are continuous, but they can also be categorized into 4-level categorical variables each level being above a threshold for severity of symptoms. There are 10 symptoms and I have a very small sample of just above 60 patients. The model seems to work much better when I use categorical variables. It fits well into a (clinically sound) 2 class solution as indicated by several fit indices and likelohood tests. When I use variables as continuous the model never fits and all k+1 classes solution is always better than k classes.
My questions are: 1)Why is this happening? 2) when reporting this in a manuscript, how can I justify for using the categorical vs the coninuous variables?