Hi, I have a problem with a confirmatory analysis. I have 40 items of which 10 were strong, but the maximum likelihood analysis tells me that I must count 4 factors of these 10 items, thus I am asked to repeat some items in more than one factor, my question is, ¿Is this possibile?, ¿Methodological possible?
If it is possible methodologically but it has to be according to its theoretical framework, the other thing is that it seems a second order model only that the arrows are inverted between the factors and the general variable, I don't think its model is correct
Hello Jesua,
So, for items like AP3 and AN1, were these cross-loadings implied by your theory, your instrument construction, or by you paying attention to suggested modification indices from your CFA software? As Cristian notes, choices need to be consistent with the underlying basis for your measure(s). In your case, this would apply to both the cross-loadings and the second-order factor solution.
So, to answer your question, is it possible? Absolutely. Is it mandatory to have these? Not at all. It may be better to modify (and retest) or discard items that do not conform to the theoretical framework than to re-imagine the framework to fit the behavior of the data. The latter approach leads to over-fitting, which tends not to generalize well to other samples and data sets.
So, think carefully about whether each change you make is consistent with the intended basis and behavior of the measure.
Good luck with your work.
As pointed out, consider redoing the analysis. G is the super-factor and the arrows should be pointing toward the four sub-factors, not away from them. What this model is telling me that that G is a "super-item" that has cross-loadings on all four factors. Regarding cross-loadings of items on different factors, consider reducing the number of factors. Are the four factors justified by theory? If not, can you reduce them to three or two factors with low correlations between them? Right now, the correlations between factor 1 and 3, and between factor 2 and four are high (0.70). Alternatively, you can also consider reducing the number of (redundant) items in the model. Semantically similar items almost always lead to cross-loadings.
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