Dear Stata users, I have a question to ask concerning latent class analysis (LCA). I am working with a sample of 57 American cities. I am using a total of 83 city-level variables to place my 57 cities into a set of latent classes (I am considering first using exploratory factor analysis to aggregate my 83 city-level variables into 7 or so factors/indexes). My goal is to study how different latent classes of American cities are associated with the overall happiness of city residents. I am concerned that my sample size of 57 cities might be inadequate to develop a stable set of latent classes. I would very much appreciate it if anyone could offer me advice concerning whether an LCA can be done with a sample of 57. Might it be the case that I should limit the number of latent classes I develop to a certain number?
I am aware of some of the techniques (like BIC) used to establish what is the best number of latent classes to develop with LCA. I’m wondering if there is any way to get a sense of whether the outcomes of these techniques are stable and accurate.
I plan on doing LCA with Stata 15's 'gsem lca.'
I would very much appreciate your help, thank you in advance!
Yours sincerely,
Jason Settels
I am aware of some of the techniques (like BIC) used to establish what is the best number of latent classes to develop with LCA. I’m wondering if there is any way to get a sense of whether the outcomes of these techniques are stable and accurate.
I plan on doing LCA with Stata 15's 'gsem lca.'
I would very much appreciate your help, thank you in advance!
Yours sincerely,
Jason Settels
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