Hi all,
There are many conflicting arguments re correlated errors in CFA so I thought I'd start this thread to gauge the temperature. Really interested to hear your thoughts
Hello Amanda,
here is a thread on specifying post-hoc error covariances, their implications, causes, and dangers.
https://www.researchgate.net/post/Theoretical_assumptions_for_correlating_errors_during_SEM#view=5f35166edc797978d53fef05
If you have a small N than the chi-square test on one hand will tend to overreject correct models while at the same time having low power to correctly reject false models. You can apply the so-called swain correction to correct for the first problem but that will probably turn out that the model has problems. Adding error covariances will put make up on it but leave the most probable issue undetected and this be a misspecification of the factor structure.
HTH
--Holger
If I understand correctly, you are conducting a 2nd order CFA with five latent variables, but additional testing indicates that two of these five need an additional correlated error to improve the fit. If this correlated error is relatively large, then I believe this is another way of telling you that these two factors may not be measuring separate things because they are more highly correlated than you predicted.
You can test this by combining the two to create a our-factor model, and test the difference in chi-squares between that and the five-factor model to see if creating the additional factor (i.e., going from four to five factors) significantly increases the fit.
- Badges
- Science topic
- Similar topics
- Mathematics
- Statistics
- Sampling Design
- Sample Size