We are running ordinal EFA and CFA using WLSMV estimator to see how many factors to extract. N in this sample =550.
In >15 samples we looked at, we found either 1 or 2 factor solution, fit indices for both solutions being close to good, eigenvalue for third factor <.4 and bad fit.
This sample proves problematic, however.
EFA 1-3: STANDARD ERRORS COULD NOT BE COMPUTED. PROBLEM OCCURRED IN EXPLORATORY FACTOR ANALYSIS WITH 3 FACTOR(S). THE CONDITION NUMBER OF THE ROTATED SOLUTION IS 0.162D-11. THE OPTIMAL ROTATION IS NOT SUFFICIENTLY IDENTIFIED. CHANGING THE ROTATION METHOD MAY RESOLVE THIS PROBLEM.
Eigenvalue for third factor >1 so we're interested to extract it.
CFA: Testing 1 through 3 factor solutions with CFA shows abysmal fit indices, and this warning in 2 and 3 factor solution:
WARNING: THE RESIDUAL COVARIANCE MATRIX (THETA) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR AN OBSERVED VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO OBSERVED VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO OBSERVED VARIABLES. CHECK THE RESULTS SECTION FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE PHQ4.
Regarding the EFA, you could try changing the rotation method as is suggested.
Regarding the CFA, ask for the standardized solution where you can see if you have a negative residual variance for phq4. I suspect that is the problem.
EFried posted on Saturday, June 09, 2012 - 1:46 am
Thank you Linda. Using quartimin in EFA I get the error message that variance for PHQ4 is negative.
Using promax the EFA runs, but the results are bogus. I tried CATPCA in SPSS and checked the data twice (frequencies), there are no problems in the data, and N~500 should also be ok. Again, on the other 15 datasets both the EFA and CFA syntax in MPLUS worked very well.
You are right about the negative residual variance in the CFA. Could you recommend a way to solve this? PHQ4 Undefined 0.22316E+01 -1.232
It's way to large to just fix it to zero. Using a 1 factor solution doesn't fit at all (4 out of 9 items don't even load significantly on the one factor in that case), that's why we're trying to extract 2 or 3 factors.
To constrain residual variances to be positive in EFA, you can use ESEM, label the residual variances, and use Model Constraint to require each to be >0. That, however, may mask an important misspecification such as using too many factors or omitting correlated residuals (which can also be included in ESEM). The ESEM EFA is specified like
I have 12 items of a scale that we measured 3 times. I am trying to run longitudinal factor analysis to see how the loadings are common over the time, invariance of the factor loadings and common factors. It is example 4.5 from the manual.
I would like to know if you could explain me or help me find a place to understand: 1. Which variable should I use as clustering variable: participant ID or timepoint? 2. Understanding better the within and between factors.
I tried to run before the EFA at 2 time points with wide format (Example 5.26). When I run the syntax with t1 data, I get the message “No Convergence… The residual covariance matrix (theta) is not positive definite”. When I run the analysis with t2 and t3 data, I have no issues.
I would like to know if you could help me setting up this analysis.