I’m stuck on a Confirmatory Factor Analysis (CFA) in R. I’m looking at a psychometric scale with 6 factors and 66 items. My sample size is 200.
Here’s a snippet of my model:
model_spec <- '
Factor1 =~ Item1 + Item2 + Item3 + Item4
Factor2 =~ Item5 + Item6 + Item7 + Item8 + Item9
# ... more factors and items
'
fit <- cfa(model_spec, data = my_data, ordered = c('Item1', 'Item2', ...), std.lv = TRUE)
When I run this, I get a warning:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
is smaller than zero.
I checked for Heywood cases, but there are no negative variances or covariances above 1. What could be causing this? How can I fix it? Any help would be great!
This issue often occurs with complex models combined with small sample sizes. Your model uses 66 items and 6 factors with only 200 participants, which can lead to imprecise estimates. One approach is to reduce the model complexity by limiting the number of factors or items and seeing if the problem persists.
Another suggestion is to examine multicollinearity among the items, as high correlations might be contributing to the error. It is also useful to inspect your data for any outliers or unusual distributions. Finally, consider using an estimator that is better suited for ordinal data or, if possible, increasing your sample size. CFA is notably sensitive to sample size and model complexity, so these changes could help you overcome the issue.
hey climbing mountain, that’s a tough one! have u tried simplifying ur model? maybe start with fewer factors or items and see if that helps. also, check if any items r super correlated - that can mess things up. and don’t forget to look at ur data distribution. sometimes weird patterns can cause this. good luck!
Hey there, ClimbingMountain! 
Oof, that’s a tricky one you’re dealing with. Non-positive definite matrices can be such a headache, right?
Have you considered playing around with different estimation methods? Sometimes switching from ML to WLSMV can help, especially with ordinal data. It might be worth a shot!
Also, I’m curious - how are your items distributed? If they’re really skewed or have limited variance, that could be throwing things off. Maybe take a peek at some histograms?
Oh, and here’s a wild thought - have you tried a Bayesian approach? It can sometimes be more forgiving with smaller sample sizes. Plus, it’s kinda fun to dive into that whole world of priors and posteriors. 
Anyway, don’t let it get you down. CFA can be a beast, but you’ll crack it! Keep us posted on how it goes, yeah?