# Confirmatory factor analysis with 4 convoluted factors

I'm having some trouble with a Confirmatory Factor Analysis (CFA) and my data. I use a scale of 16 items in which one item always belongs to two out of four latent factors (SE, CE, Goal, indicated by a "g", and Others, indicated by an "o"). If I add a factor for inverse items (_u), the CFA proves a good fit. However, R returns the following warning, "lavaan WARNING: model has NOT converged!", if I try to run the original model. I think it might have to do with the fact that I don't have unique items for factors. But how do I manage this problem? And how can I include a latent factor behind the 4 factors in this model as well?

Original model:

Modell <- 'SE     =~ SEg01_u + SEg02 + SEg03 + SEg04 + SEo01_u + SEo02 + SEo03 + SEo04
CE     =~ CEg01_u + CEg02 + CEg03 + CEg04 + CEo01_u + CEo02 + CEo03 + CEo04
Goal   =~ SEg01_u + SEg02 + SEg03 + SEg04 + CEg01_u + CEg02 + CEg03 + CEg04
Other  =~ SEo01_u + SEo02 + SEo03 + SEo04 + CEo01_u + CEo02 + CEo03 + CEo04'
fit <- cfa(Modell, data = dataset)
summary(fit, fit.measures=TRUE, modindices=TRUE)


I'd be grateful for any help!

• You might do better on an R site or one specific to lavaan if such a thing exists. Feb 18 '18 at 16:23