I attended your session in Baltimore last week at Hopkins, and learned immensely. Thank you for a great presentation. I am trying to find an example in the text that fits my analysis needs. I am trying to see if there are latent groups with differing symptom experiences in a sample of patients with advanced melanoma. The data are individual items from a symptom checklist, where various symptoms are listed, and the respondent is asked to indicate the severity of the symptom on a scale of 0-4. 0 is if they don't have the symptom, and 4 is the worst severity possible. As you might expect, there are a preponderance of zeros in this data set, and so I have been looking at whether a 2 part model is the right approach. I can find some examples of a 2 part model for longitudinal data but I am hoping you can point me to a paper or examples in your text of this approach for a cross-sectional analysis. Thank you in advance for your help. Sandra
We are finishing up a paper on 2-part factor analysis and the steps to do this, so that's a cross-sectional analysis that might be helpful. It will be posted shortly.
However, unless you have a strong reason to use 2-part in this example, I think perhaps it more straightforward to treat your outcome as (ordered) categorical. The LCA approach for this is just like for binary outcomes.
2-part is more suitable when you have a more continuous tail for the non-zero part - you have only the categories 1, 2, 3, 4. Also, for 2-part you might want to have a hypothesis that the choice of being in zero vs not zero has different antecedents than the non-zero values.
If you treat your outcome as categorical you may want consider the need to collapse the 2 highest categories to not have too few observations there.