I’m stuck with a tricky CFA model in lavaan. I’m looking at the Social Dominance Orientation (SDO) scale. The model has four factors: dominance, antiegalitarianism, pro-trait, and con-trait. Items are supposed to load on multiple factors.
Here’s what I’ve tried:
model_sdo <- '
Factor1 =~ Item1 + Item2 + Item3 + Item4 + Item5 + Item6 + Item7 + Item8
Factor2 =~ Item9 + Item10 + Item11 + Item12 + Item13 + Item14 + Item15 + Item16
Factor3 =~ Item1 + Item2 + Item3 + Item4 + Item9 + Item10 + Item11 + Item12
Factor4 =~ Item5 + Item6 + Item7 + Item8 + Item13 + Item14 + Item15 + Item16
'
fit_result <- cfa(model_sdo, data = my_data)
But it’s not working. The optimizer keeps running without finding a solution. I’ve tried over 3000 iterations.
Is my model specification wrong? How can I get this to converge while keeping the complex factor structure? Any tips would be super helpful!
yo, BrilliantCoder23! that SDO model’s a beast, huh? 
have u tried tweaking ur starting values? Sometimes that helps kickstart convergence. Also, maybe check for outliers in ur data? They can mess things up big time. if nothing else works, u might need to rethink the factor structure. hang in there, CFA’s can be a pain but u got this!
Hey there, BrilliantCoder23! 
Your CFA model for the SDO scale sounds pretty complex - no wonder you’re having convergence issues! Have you considered simplifying your model a bit to start? Maybe try running separate models for each factor first and see how they behave?
I’m curious, have you looked at the modification indices or residual correlations? Sometimes they can give clues about where the model might be struggling. Also, what about your sample size? With complex models like this, you might need quite a large sample to get stable estimates.
Oh, and here’s a thought - have you tried different estimators? Sometimes switching from ML to WLSMV can help with tricky models, especially if your items are ordinal.
What do you think about these ideas? I’d love to hear more about your data and what you’ve tried so far. Hang in there - CFA can be a beast, but we’ll figure it out!
Having worked with complex CFA models, I can sympathize with your convergence issues. One approach that’s proven effective is to start with a simpler model and gradually increase complexity. Perhaps begin with a two-factor model, then incrementally add the other factors.
Another strategy is to examine your data closely. Check for multicollinearity among your items, as this can cause convergence problems. You might also consider using parceling techniques to reduce the number of indicators per factor.
Regarding your lavaan syntax, ensure you’ve specified the model correctly. Double-check that all paths are defined as intended. Sometimes, minor syntax errors can lead to major headaches.
Lastly, if all else fails, you might need to reconsider your theoretical model. Is it possible that a different factor structure might better fit your data? Sometimes, empirical results suggest refinements to our conceptual frameworks.