Dear lavaan community,
I am new to lavaan and would appreciate your guidance on the following issue.
When I try to test the simple two-factor CFA model shown below using the WLSMV option in lavaan in a large dataset (n = 1 523) with no missing values, I get the warning message shown in red below.
> CFAmodel2 <- '
SQ_SAT =~ Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6
Loyalty =~ Q5_1 + Q5_2 + Q5_3 + Q5_4
'
> fit <- cfa(CFAmodel2, data=my_data, estimator="WLSMV", ordered = c("Q3_1", "Q3_2", "Q3_3", "Q3_4", "Q3_5", "Q3_6", "Q5_1", "Q5_2", "Q5_3", "Q5_4"))
Warning message:
In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, :
lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= 9.027647e-18) is close to zero. This may be a symptom that the
model is not identified.> summary(fit, fit.measures = TRUE, estimates=TRUE, standardized = TRUE, rsquare = TRUE, nd=5)
I have the following questions:
1. Can I ignore this warning message and use the model estimates produced by lavaan?
2. If not, which alternative estimator should I rather use given that the manifest variables were all measured with 5-point Likert scales? The responses on these scales all have a strong "ceiling effect" with more than 90% of respondents having selected either a 4 or a 5 on the 5-point scale in all cases.
3. Is there some other way to resolve the issue causing warning message?
Thank you for your inputs.
Kind regards,
Theuns