I am examine psychometric properties of my model using CFA. I have 7 factors and 2 dimensions of motivation. Should i analyze them separately using 2 model of CFA?
One model for factors and another one for dimensions, or just analyze using 1 model?
FYI the factors and dimensions measuring different aspect of motivation.
It would almost certainly be a stronger test of the psychometric properties of your scales if you included all factors in one single model (as long as the resulting model is feasible given the sample size). The combined model has more degrees of freedom and therefore provides for a stronger test of model fit.
Also, by including everything in a single model, you can examine whether there are any cross-loadings of items on multiple factors or other measurement-related issues (e.g., residual covariances). You can also assess discriminant validity by looking at the factor correlation estimates, which you could not do if you analyzed each factor/scale separately.
In Confirmatory Factor Analysis (CFA), the decision to analyze factors separately or together depends on the research question and the theoretical framework of your study.
If the two dimensions of motivation are theoretically distinct and you expect them to influence the factors differently, it might make sense to analyze them separately. This approach allows you to examine the unique contribution of each dimension to the factors.
On the other hand, if the two dimensions of motivation are closely related or intertwined, analyzing them together in a single model might be more appropriate. This approach allows you to examine the combined effect of the dimensions on the factors.
Remember, the choice between these two approaches should be guided by your theoretical framework and research question, not just statistical convenience. It’s always a good idea to consult with a statistician or a methodologist if you’re unsure.
Also, consider checking the model fit indices (like RMSEA, CFI, TLI, SRMR) for both models to see which one fits the data better. But remember, a good fit does not necessarily mean the model is theoretically sound or meaningful. Always interpret the results in the context of your study.
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