Oct 23, 2019 10:55 AM
| Last Modified: Oct 23, 2019 12:39 PM(648 views)
I want to perform a latent class analysis on my mixture-amount experiment in JMP. Under the tab 'analyze', 'consumer reasearch', 'choice' I know how to perform a conditional logit model (via the Firth Bias-adjusted estimates) and a mixed logit model (via the Hierchical Bayes estimation) but I was wondering if performing a latent class analysis was also possible for choice experiments with JMP.
I tried already the tab 'analyze', 'clustering', 'latent class analysis' but there I cannot specify the specific mixturemodel and I cannot state that it was a discrete choice experiment.
Is there a solution within JMP to perform a latent class analysis for a discrete choice experiment?
Latent class analysis in JMP is a kind of un-supervised learning method. The model yields classes that can then be used as responses or predictors. I would use the classes as levels of a response versus the mixture amounts in your case.
Thank you for your answer! However, I don't really understand what you mean. I included my dataset below.
Do you mean that I first need to perform a latent class analysis (under 'analyze', 'clustering') with the variables 'Chosen', 'Choice card' and 'Block' in my data as categorical response variables (and 'Respondent' as ID)? Then, I can save the probability for each respondent for each choice set to belong to a certain class. But how can I then use the classes as levels of a response versus the mixture components via 'analyze', 'consumer research', 'choice'?
My apologies. I was only trying to explain LCA. No, I do not think that it would help you in this case. The choice experiments and modeling are pretty well defined. I think that there might be loss of information if you converted your responses to classes first.