Factor loadings in a CFA with multiple correlated latent factors are akin to regression estimates not correlations, so having a loading over 1 is possible. Whether or not it is a concern is hard to say without more context.
Factor loadings in a CFA with multiple correlated latent factors are akin to regression estimates not correlations, so having a loading over 1 is possible. Whether or not it is a concern is hard to say without more context.
Thanks. I'm cross validating two questionnaires and the > 1 loadings come from an established big five inventory. If loadings are akin to regression estimates, I suspect they might be able to amount to > 1 if the items are highly correlated?
Thanks. I'm cross validating two questionnaires and the > 1 loadings come from an established big five inventory. If loadings are akin to regression estimates, I suspect they might be able to amount to > 1 if the items are highly correlated?
Not quite. Remember the diagrams of a CFA. The arrows go from the latents to the items not the other way around. Hence, correlation between the items is not the issue here but the correlation between the latents.
In passing with measure of the big 5 you might want to try EFA or ESEM for more complicated models given known issues with model fit for the Big 5 in CFA models.
Not quite. Remember the diagrams of a CFA. The arrows go from the latents to the items not the other way around. Hence, correlation between the items is not the issue here but the correlation between the latents.
In passing with measure of the big 5 you might want to try EFA or ESEM for more complicated models given known issues with model fit for the Big 5 in CFA models.
