Below is the syntax for a CFA I'm running. I'm trying to constrain residual variance for variables y2 and y10 to zero as they are negative but non-significant. Can you help me understand what syntax to use. I'm having a hard time understanding based on example 5.20 in the manual.
TITLE: Measurement Model CFA
DATA: FILE IS C:\DissData.dat; VARIABLE: NAMES ARE y1-y20; MODEL: F1 BY y1, y2, y20; . . . F7 BY LogY10, LogY11;
From reading the discussion board, it seems that if there are residual variances that are negative, then it is possible to constrain these variances to 0 (or is it to 1?) and re-run the CFA.
However, it also seems that this is not considered "best" practice and may be more appropriate to run an EFA. Can you help me understand how running the EFA will be helpful? What information will it provide?
My original CFA was not converging due to negative residual variance. I conducted an EFA (in SPSS) and modified four of the indicators to fit my data more appropriately. I re-ran the CFA and 3 out of 4 indicators were no longer negative. I decided to remove the factor associated with that indicator that still has negative residual variance. Then ran the CFA and although there is NO negative residual variance, the model is still not converging because of too many iterations. Can you help me determine why? And what to do????
This points to the need for a change in your model. I would do an EFA in Mplus. There is a big difference between an EFA and a CFA. If the EFA is not clean, fixing all of the factor loadings to zero in a CFA may not be the best way to go. You can send the EFA output, CFA output, and your license number to firstname.lastname@example.org if you want further guidance.