You can use either. If you have several factors, WLSMV is best because with ML each factor with binary factor indicators requires one dimension of integration. If you want to include residual covariances between factor indicators, WLSMV is also best because with ML each residual covariance requires one dimension of integration. Models using more than four dimensions of integration are not recommended.
Scott Smith posted on Monday, October 14, 2013 - 11:25 am
Can you further explain what dimensions of integration are? Does this mean that I shouldn't run more than four factors within one CFA model at a time?
I'm performing CFA with all binary variables. I have a problem becouse I have to set to 1 the variances of the three latent variables (all latent variables) to estimate the model, and I think that this is the reason to obtain the same results in Model results and Standardized estimates.
I need the results of the STDYX output for this model and I have 7 standarized estimates greater than 1, e.g x4 has a estimate=1.141 and S.E.=0.177
I´m wondering if you know some formula to fix the standardized estimates in the STDYX and obtain values less than 1.
Standardized coefficients can be greater than one. See the FAQ on our website.
Danica Cruz posted on Sunday, August 03, 2014 - 6:53 pm
I read in the Mplus User's Guide that it's possible to have combinations of continuous, binary and categorical indicators in a measurement model. Is it possible to have these combinations on the same factor? For example a binary variable and two categorical variables (one with 4 levels and one with 5 levels)? If so, how would the scaling work? Thank you.