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?