I’m running into issues with my confirmatory factor analysis using Lavaan in R. After 2044 iterations, the model isn’t converging. Here’s a simplified version of my code:
library(lavaan)
data <- read.csv("wine_survey.csv")
model <- '
WineBehavior =~ Purchase + Consume + ClubInterest
ProductAspects =~ General + Features + AppUse + Taste
BuyingIntent =~ Intent1 + Intent2 + Intent3
BuyingIntent =~ WineBehavior + ProductAspects
'
fit <- cfa(model, data = data)
summary(fit)
The error message says ‘lavaan did NOT end normally’. Any ideas what might be causing this? Could it be an issue with my model specification or the data itself? I’ve double-checked for missing values and outliers. Any suggestions for troubleshooting would be great. Thanks!
Having encountered similar issues, I can suggest a few troubleshooting steps. First, check your sample size. CFA models often require larger samples for convergence. If your sample is small, consider simplifying your model.
Next, examine your model specification. The line ‘BuyingIntent =~ WineBehavior + ProductAspects’ looks suspicious. Typically, you’d define latent variables with their indicators, not other latent variables. This could be causing issues.
Also, try standardizing your variables before analysis. This can sometimes help with convergence. You might also want to inspect your correlation matrix for any extremely high correlations that could indicate multicollinearity.
If these don’t work, consider using modification indices to identify problematic paths or cross-loadings. Remember, though, any changes should be theoretically justified.
Lastly, you could try a different estimator. The default maximum likelihood might struggle with your data. WLSMV or robust MLM could be worth a shot.
hey, i’ve had similar problems. have u tried using a different estimator? sometimes the default one doesn’t work well. Also, check ur model specification. The line ‘BuyingIntent =~ WineBehavior + ProductAspects’ looks weird. usually u define latent vars with indicators, not other latent vars. That might be causing issues. good luck!
Hey there, fellow R enthusiast! 
Your CFA conundrum sounds super interesting. Have you considered that your model might be a bit too complex? Sometimes less is more, you know? 
I’m really curious about your data. How many responses do you have in that wine survey? And what made you choose this particular structure for your model? It’s always fascinating to hear the reasoning behind these decisions!
One thing that caught my eye – that last line in your model specification looks a bit unusual. Typically, we don’t connect latent variables like that in CFA. Maybe try removing ‘BuyingIntent =~ WineBehavior + ProductAspects’ and see what happens?
Oh, and have you played around with the lavaan options at all? Sometimes tweaking things like the optimization algorithm or increasing the number of iterations can work wonders.
Keep us posted on how it goes! I’m sure with a bit of tinkering, you’ll crack this wine-derful puzzle. 
