Derek posted on Monday, October 12, 2015 - 7:13 pm
Thanks for your help in advance. I am running a Latent Class Analysis Model with 3 dummy variables and 3 nominal variables (1 is ordinal in nature and 2 are truly nominal).
I am wondering how I can best interpret the obtained probabilities from the LCA model with nominal indicators. I believe the graph/plot approach is only available for binary indicators but not applicable to nominal indicators (Please correct me if I am wrong here; I hope I am wrong).
For example, the below are probabilities from a three-class model. Please help me interpret the probabilities associated with "people" (4 categories--nominal), "location" (4 categories--nominal) and "weapon" (3 categories--ordinal)
For each column (latent class) the 4 values corresponding to the 4 nominal categories sum to 1. For each column you want to see where the big probabilities are. Take people as an example. So class 1 people tend to have the highest prob for answering category 4, class 2 people tend to have the highest probs for cats 1, 2 and 4, etc
Derek posted on Wednesday, October 14, 2015 - 3:26 pm
Thanks a lot for your answer, Dr. Muthen. An additional question is: is it also necessary to compare horizontally? (in other words, to compare across latent classes?)
For example, for "weapon", the overall sample has a very low probability of carrying a gun or other weapon. If we only compare within class, we would say people from all three classes are low-risk there. But, as you can see, when we make horizontal comparisons, class 3 exhibits higher probabilities of both carrying a gun or other weapon (for the whole sample: 0.006, 0.013, 0.982). Should this kind of information also be taken into account when we assign meaningful labels to different classes and understand the nature of the classes?
In general, are nominal variables likely to lead to low homogeneity due to multiple categories?