Are composite variables (eg. a score) valid to perfom a SEM in AMOS? I have a resilience scale with 11 items (observed variables), from which I calculated the mean to get a "resilience score" (what would usually be an unobserved variable or latent construct in SEM terminology). 


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*Can I just use this resilience score to build a SEM in AMOS or do I need to include the 11 items as well ? 

*If I just included the resilience score as an observed variable, would it be considered a limitation or would it be all wrong? 

*In general, Is it valid to include a composite variable in a structural equation model in AMOS when such variables are treated as observed variables? 

 A: In general, structural equation modelling (SEM) with all observed variables is typically called path analysis.
One of the main motivations for SEM is to attempt to model relationships between latent variables. By including items rather than the composite score and modelling items as indicators of a latent variable you are able to assess relationships between latent variables.
In particular, with items rather than the composite score


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*you can assess your measurement model

*you can get an estimate of relationships between latent variables (i.e., adjusting for measurement error).


Various middle grounds also exist including:


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*item parcelling: i.e., you create two or more parcels of items from your 11 items, and include these parcels as observed variables for a latent variable.

*incorporate error of measurement into the model with observed variables.


It is not "invalid" to include a composite variable in SEM. However, it is in some sense invalid to say that inferences based on the observed composite variable are representative of the relationship between theorised latent variables. Most of the time, you'd want to adopt one of the other approaches (i.e., including items, including item parcels, or include measurement error).
A: If this is truly a scale, and the 11 observed items are endogenous, then your score contains a measurement error, and putting it into a regression model leads to biases: your estimates will be shrunk towards zero (see http://www.citeulike.org/user/ctacmo/article/2663962). This is a poor man's strategy for somebody who has SPSS, but does not have AMOS. If you have AMOS, there is little excuse in putting together a model that uses composite scores, rather than a full SEM that incorporates the measurement model for these 11 items. Besides improvements in accuracy of the estimates, you will also get the overall fit test that would allow you to judge how well your model fits the data.
