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I tried a confirmatory factor analysis with 8 variables and 3 latent variables. it gave the following warning- :

Warning messages:
1: In lav_data_full(data = data, group = group, cluster = cluster,  :
  lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
2: 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
    (= -2.383743e+03) is smaller than zero. This may be a symptom that
    the model is not identified.
3: In lav_object_post_check(object) :
  lavaan WARNING: some estimated ov variances are negative

In order to reduce scale the variables into relatively similar scale I used kmean clustering and arranged them in ascending order of numeric scale of 1-7. However, cfa is now unable to converge and I ended up with the following error :

Warning message:
In lav_object_summary(object = object, header = header, fit.measures = fit.measures,  :
  lavaan WARNING: fit measures not available if model did not converge

I tried a confirmatory factor analysis and was expecting a good fit to continue towards SEM modelling.

Phil
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Sujeet Sahoo
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  • As per the message, you have factor variables with far too many levels. Check your data to see if they were numeric variables accidentally set to factor. If they are set up how you mean it, you will have to collapse the factor levels such that there is enough data in each level to properly run the analysis. – Phil Jul 26 '23 at 14:22

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