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Hello, I am working on some invariance across time analysis. First I ran a simple CFA for one construct with 4 indicators at Time 1. When I ran the model, I received a warning that the model was not identified. I decided to add a syntax line specifying that the mean of the latent construct is fixed at zero. Once I ran the syntax again, the model worked fine and I got no warnings. I am not sure I understand why adding that line of syntax made a difference as I thought that it was the default in Mplus 6.1 for the mean to be fixed at zero to begin with. 


In a crosssectional model the means of the factors are fixed at zero for model identification. Only in a multiple group or multiple time point model can factors means be identified in all but one group or all but one time point. 


Thank you for the quick response. If I run a model with the following syntax, where I ask for the means of the latent construct at each time point with the mean at time 1 fixed at 0, I receive an warning message stating that the model is not identified. However, if I remove the line [IDT1@0 IDT2 IDT3]; then there is no problem. But isn't the above line simply stating the same thing as the default settings in the program? So why should it make a difference whether I have that line in or not? MODEL: !Identity at Time 1 IDT1 by ID055* ID058 ID059@1 ID0511 ID0513 ID0514 ID0529; !Identity at Time 2 IDT2 by ID061* ID062 ID063@1 ID064 ID065 ID066 ID0610; !Identity at Time 3 IDT3 by ID075* ID078 ID079@1 ID0711 ID0713 ID0714 ID0729; !Means & Intercepts [ID055 ID061 ID075]; [ID058 ID062 ID078]; [ID059 ID063 ID079]; [ID0511 ID064 ID0711]; [ID0513 ID065 ID0713]; [ID0514 ID066 ID0714]; [ID0529 ID0610 ID0729]; [IDT1@0 IDT2 IDT3]; !Variances & Residual Variances ID055 ID061 ID075; ID058 ID062 ID078; ID059 ID063 ID079; ID0511 ID064 ID0711; ID0513 ID065 ID0713; ID0514 ID066 ID0714; ID0529 ID0610 ID0729; IDT1 IDT2 IDT3; !Correlations & Covariances IDT1 with IDT2 IDT3; IDT2 with IDT3; 


For the factor means to be identified, the intercepts must be held equal over time. See multiple indicator growth modeling in the Topic 4 course handout on the website. The inputs to test for measurement invariance across time are shown as part of this example. 

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