I have a sample (n=200) that I have collected questionnaire data from. Each participant will complete 5 questionnaires that capture different behaviours and all of my 5 questionnaires include between 10 and 27 items.
I am planning to use SEM to construct a model that investigates whether scores on 4 of my questionnaires (IVs) predict scores on the 5th questionnaire (DV). The IVs and DV will be treated as latent variables in the model, each with a number of indicators attached.
However, because my questionnaires have so many items I want to engage some sort of process of reduction so that I have less indicators in my SEM. I'm wondering how my rationale holds, as I'm new to this.
What I am considering at the moment is conducting an exploratory factor analysis and then confirmatory factor analysis on each of the 5 questionnaires separately to extract how many factors are captured by the items (lets say for example, that magically each questionnaire has 3 underlying factors). I would then sum the scores of the items that are associated with each of the 3 factors separately, so each participant now has 3 scores from each questionnaire rather than the 1 total sum score.
The 3 scores would then form 3 indicators used in the final SEM predictive model. In my predictive SEM model, I would have 5 'latent variables', each loading on 3 indicators.
I guess the central question is: If you have in excess of 10-items associated with a latent variable, is it acceptable to use derived factor scores as indicators in SEM rather than the original items?