Thank you very much! I have run the models. One little confusion has occured. Using WLSMV Mplus reports the cross-equation correlations. However, it does not report those using ML (logit and probit). What can I do to get the cross-equation correlations using ML option?
When specifying an ordered logistic model with categorical explanatory variables, is my interpretation correct that the reported coefficients for the explanatory variables represent the log odds ratios of "0" versus "1" for the dummy-coded explanatory variables (i.e., the reference category for the dummy-coded explanatory variable is "1")? The signs of the coefficients are opposite from what I would expect if the reference category were "0" for the explanatory variables.
The problem I am working on involves correlated ordinal responses to numerous survey questions. All the survey questions are driving toward a similar theme (effectiveness of technical assistance) but they ask about various skills and services the TA provided. It appears multivariate ordered probit or logit is one way to proceed.
The above setup by Armen looks very user-friendly. Will Mplus use markov-chain monte carlo simulation to maximize the likelihood function. How can I read more on what mplus is doing in the background?
Mplus does not do Bayesian estimation using Markov-Chain Monte Carlo. For ordinal responses, probit regression is available using a weighted least squares estimator or a maximum likelihood estimator and logistic regression is available using a maximum likelihood estimator. See the technical appendices on the website for further details.
Sarah Ryan posted on Wednesday, February 29, 2012 - 11:50 am
Linda stated, in 2006:
"I think by marginal effects you mean derivatives or elasticities. These are not provided by Mplus."
Is this still the case? A reviewer is asking for average marginal effects (my work is based on a structural regression model using WLSMV).