I’m working on a confirmatory factor analysis using R’s lavaan package. My code isn’t working as expected. After 2044 iterations, it’s giving me an error saying it didn’t end normally. I’m not sure what’s causing this issue.
Here’s a simplified version of what I’m trying to do:
library(lavaan)
my_data <- read.csv('wine_survey.csv')
model_spec <- '
Buying =~ q1 + q2 + q3
Drinking =~ q4 + q5 + q6
ClubMembership =~ q7 + q8 + q9
WineEngagement =~ Buying + Drinking + ClubMembership
ProductKnowledge =~ q10 + q11 + q12
Experience =~ q13 + q14 + q15
Purchase =~ q16 + q17 + q18
Purchase =~ WineEngagement + ProductKnowledge
'
results <- cfa(model_spec, data = my_data)
summary(results)
Can anyone spot what might be wrong? I’m new to CFA and could use some guidance. Thanks!
I’ve encountered similar issues with lavaan before. One potential problem could be multicollinearity among your variables. This often leads to convergence failures, especially in complex models like yours.
To troubleshoot, I’d suggest examining the correlation matrix of your variables. Look for extremely high correlations (>0.9) between predictors. If present, consider removing one of the highly correlated variables or combining them into a composite score.
Another approach is to check for Heywood cases - these are instances where the model estimates a negative variance or a correlation greater than 1, which is theoretically impossible. Lavaan usually flags these, but they can cause non-convergence.
Lastly, ensure your sample size is adequate for the model complexity. As a rule of thumb, aim for at least 10 observations per estimated parameter. If your sample is too small, you might need to simplify your model or collect more data.
Hope this helps point you in the right direction for resolving your convergence issues.
Hey DancingButterfly! 
I’ve been there with lavaan and CFA troubles. It can be so frustrating when your model won’t converge!
Have you checked your data for any oddities? Sometimes weird outliers or missing values can throw things off. Also, your model looks pretty complex - maybe it’s too ambitious for the amount of data you have?
I’m curious about your sample size. How many responses are in your wine_survey.csv? CFA can get finicky with smaller samples.
Another thought - are all your variables on the same scale? If some are way bigger or smaller than others, it might help to standardize them first.
Oh, and have you tried running a simpler version of your model first? Like, maybe start with just the first-order factors and build up from there? Sometimes that can help pinpoint where things are going wonky.
Let me know if any of that helps or if you want to bounce around more ideas! CFA can be a beast, but we’ll figure it out 
yo dancingButterfly, cfa can be a pain! Have u tried adjusting ur starting values? sometimes that helps convergence. Also, check for any weird correlations in ur data - might be messing things up. if all else fails, try simplifying ur model a bit. good luck!