I am running a LCA with both binary (categorical) and nominal indicators (unordered). How do I interpret the LCA results for the nominal indicators? I get means for each nominal category intstead of thresholds. How do I compare results from nominal indicators across different classes? Is it possible to get Odds Ratios for them?
They are logits. You can turn them into odds ratios or probabilities. See Chapter 13, Calculating Probabilities for Logistic Regression Coefficients. The example with all covariates at zero would apply.
I apologize if I am about to assault you with a barrage of stupid questions, but I am relatively new to latent class models.
I realize that LCA uses categorical indicators whereas LPA uses continuous indicators. Also, I am aware that LPA makes the "strong" assumption that covariates temporally precede the latent categorical variable.
My dilemma: I specified a LC model with 4 indicators: 3 indicators have ordered categorical response options, and the other indicator is continuous. I was able to determine the # of latent classes w/ ease, and the baseline latent class solution has excellent entropy.
Would this be more aptly classified as a LPA instead of a LCA even though it has only one continuous indicator? Or is there a more appropriate name for this type of model? If it is not a true LPA, would this model also include the assumption that covariates temporally precede the latent categorical variable (given the continuous indicator)?
Incidentally, recoding the continuous variable into a categorical variable would not be a theoretically sound strategy in this case.