In a conditional model, intercepts are reported not means. Means are not model parameters in a conditional model.
Ryan Krone posted on Saturday, October 18, 2014 - 1:05 pm
Right, so these intercepts can be interpreted as the intercepts produced by the regression of the latent factor on the covariates. And the number that the output produces is really the difference between the intercepts across both groups.
I have tested for measurement invariance of a 12-item scale (2 factors) across three educational groups. The factor loadings, thresholds, residual variances appear to be similar across groups. Now I want to compare the 2 factor means in the strict factorial invariance model (WLSMV, theta parameterization). But only after having added two covariates. From the above I understand that in a conditional model the factor means are not produced, and this is also what I encountered: only intercepts and residual variances are shown for my two factors. Is it possible to obtain the means - adjusted for my covariates - in another way?
Thank you Linda, What I forgot to mention is that the factor scores were from a model that did not contain the covariates. Should the group means of these factor scores (without covariates) not be the same as the ones shown in my Mplus model (without covariates)?