I’m working on a structural equation model using the sem package and I’ve run into some problems. When I run the model, I get this warning:
Warning: Could not compute QR decomposition of Hessian.
Optimization probably did not converge.
When I try to get a summary, I see this error:
Error: coefficient covariances cannot be computed
Warning: singular Hessian: model is probably underidentified.
My model looks at how Market Orientation, Government Incentives, Entrepreneurial Orientation, and Firm Performance relate to each other. These are second-order constructs with several sub-constructs and observed variables under them.
I’ve set up my model with a bunch of paths between constructs, sub-constructs, and observed variables. There are about 56 observed-to-latent paths, 11 sub-construct-to-construct paths, and 5 paths between the main constructs.
I’m not sure what these errors mean or how to fix them. Any ideas on what might be causing these issues and how I can resolve them? Thanks for any help!
yo, those errors can be a real pain. sounds like ur model might be too complex. try simplifying it by cutting down some paths or combining constructs. also, check if u have enough data for all those variables. sometimes starting with a simpler model and gradually adding complexity helps. good luck with ur analysis!
These errors are common in complex SEM models, especially when dealing with higher-order constructs. In my experience, problems with model identification can be a significant issue. When there are too many paths or constructs, the model might be underidentified, so simplifying it by reducing unnecessary paths or combining constructs could help. Additionally, multicollinearity between variables may lead to estimation problems, and having too few observations relative to the number of parameters is another common pitfall. Adjusting the starting values could improve convergence, as well as ensuring that the variables are scaled similarly. If these steps do not resolve the issue, consider revisiting the model structure or exploring alternative estimation methods like Bayesian techniques.
Hey there, GracefulDancer8! 
Those error messages sound super frustrating. I’m curious about a few things that might help us figure this out:
How many observations do you have in your dataset? With all those paths and constructs, you might need a pretty big sample size to make it work.
Have you tried running a correlation matrix on your variables? Sometimes high correlations between predictors can cause these kinds of issues.
Also, what made you choose this particular model structure? It sounds pretty complex - is there a way you could simplify it a bit without losing the core of what you’re trying to investigate?
I’ve had similar headaches with SEM before, and sometimes it helps to take a step back and rethink the approach. Maybe start with a simpler version of your model and build up from there?
Let me know if you want to brainstorm some ideas! It’d be interesting to hear more about your research too. 