Optimization algorithm CFA / LCA PreviousNext
Mplus Discussion > Confirmatory Factor Analysis >
 Chris Gaasendam posted on Friday, April 29, 2016 - 2:18 am
I am writing an article about the comparison of LCA and CFA. I have several questions about the computation of the parameter estimates in the different analyses.

- to my understanding the optimization algorithm used to estimate parameters in LCA is (based on) iterative proportional fitting; Mplus, however, uses the more broadly applicable accelerated expectation maximization algorithm in conjunction with Fisher Scoring and Quasi-Newton. For my five-group model with 18 indicators with each 3 categories, once I get to >4 class models (which the output suggests me to explore) the analysis becomes too computationally demanding (days), and even with very high start values (starts = 6000 1500; for 5-class model) Mplus cannot verify to have converged to the global maximum. Might I better switch to a faster algorithm, and if so, which one would that be in Mplus?
- although I was not able to verify this from Mplus output or User Manual, I assume continuous-variable CFA also uses EMA; is it then safe to say that CFA and LCA use the same optimization algorithm for ML estimation despite the differences in variable distribution?

Thanks in advance for your reply.


Back to top
Add Your Message Here
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Options: Enable HTML code in message
Automatically activate URLs in message