I am conducting a structural equation model with three latent variables containing three indicators each. The latent variable CFA's are significant with constraints on 2/3 indicators, but the SEM is significant with only 1/3 constraints on the indicators for each of the three latent variables. Is it okay to use latent variables in the SEM model that have different constraints, or do the latent variables have to fit in the SEM in the same form that they were significant, as per CFA?
Hi Samantha,
I do not quite understand what you mean with "the cfa is significant" and "constraints on x indicators". Could you be more specific? What is the fit and exact structure/characteristics of the model(s)?
Best,
Holger
So the document labeled "Dissertation CFA Models" contains the three latent variables, which were significant when confirmatory factor analysis was performed on them with the regression weight constraints pictured (regression weight constrained to 1 on two out of three of the indicators of each of the three latent variables). Then, the document labeled "SEM fit" is the three latent variables applied to a structural equation model, but in order for the model to have good fit, the latent variables have each been changed to have constrained regression weights on only one indicator for each of the three variables. This was changed from the original regression weight constraints in the CFA of the latent variables individually (prior to being put into the SEM).
Actually, see this SEM model instead. The regression weights are entered so that you can see the difference in constraints of regression weights from individual latent variable CFA.
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