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
- 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:
- 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).