1. Go back to Exploratory Factor Analysis
If you're getting very bad CFA fits, then it's often a sign that you have jumped too quickly to CFA. You should go back to exploratory factor analysis to learn about the structure of your test. If you have a large sample (in your case you don't), then you can split your sample to have an exploratory and a confirmatory sample.
- Apply exploratory factor analysis procedures to check whether the theorised number of factors seems reasonable. I'd check the scree plot to see what it suggests. I'd then check the rotated factor loading matrix with the theorised number of factors as well as with one or two more and one or two less factors. You can often see signs of under or over extraction of factors by looking at such factor loading matrices.
- Use exploratory factor analysis to identify problematic items. In particular, items loading most on a non-theorised factor, items with large cross-loadings, items that don't load highly on any factor.
The benefits of EFA is that it gives a lot of freedom, so you'll learn a lot more about the structure of the test than you will from only looking at CFA modification indices.
Anyway, hopefully from this process you may have identified a few issues and solutions. For example, you might drop a few items; you might update your theoretical model of how many factors there are and so on.
2. Improve the Confirmatory Factor Analysis Fit
There are many points that could be made here:
CFA on scales with many items per scale often perform poorly by traditional standards. This often leads people (and note I think this response is often unfortunate) to form item parcels or only use three or four items per scale. The problem is that typically proposed CFA structures fail to capture the small nuances in the data (e.g., small cross loadings, items within a test that correlate a little more than others, minor nuisance factors). These are amplified with many items per scale.
Here are a few responses to the above situation:
- Do exploratory SEM that allows for various small cross-loadings and related terms
- Examine modification indices and incorporate some of the largest reasonable modifications; e.g., a few within scale correlated residuals; a few cross-loadings. see
modificationindices(fit) in lavaan.
- Use item parcelling to reduce the number of observed variables
General comments
So in general, if you're CFA model is really bad, return to EFA to learn more about your scale. Alternatively if your EFA is good, and your CFA just looks a little bad due to well known problems of having many items per scale, then standard CFA approaches as mentioned above are appropriate.
Best Answer
Since the factor scores are a linear function of the observables, once you've calculated them once, you can simply use
lmto fit a linear regression between the fitted scores and the observables. The regression will give your an $R^2$ of $1$ and you can just read off the linear coefficients. You can then recreate the scores directly from the observations in excel or elsewhere.You only need the horribly complicated linear algebra to calculate them the first time - so as long as you are not refitting the model in excel, you can just use the linear form directly.