I’m having trouble with my structural equation model (SEM) using the sem package. The model runs but gives this warning:
Warning: Could not compute QR decomposition of Hessian.
Optimization probably did not converge.
When I try to get a summary, I get another error:
Error: coefficient covariances cannot be computed
Warning: singular Hessian: model is probably underidentified.
My model looks at Market Orientation, Government Incentives, Entrepreneurial Orientation, and Firm Performance. Each has sub-constructs with observed variables.
I’ve set up the model with around 56 observed-to-latent relationships, 11 subconstruct-to-construct connections, and 5 paths between main constructs. There are also error terms defined.
What could be causing these errors? How can I fix my model? Any advice would be really helpful!
During my own research with SEM models, I’ve encountered warnings similar to the Hessian decomposition issue. In my case, the complexity of the model was a key factor. I had to simplify the model by reconsidering less critical paths and combining closely related subconstructs to avoid overparameterization. Reviewing the sample size also proved beneficial, since insufficient data relative to the number of parameters can lead to convergence problems. In some instances, switching to another estimation method appropriate for the data type helped resolve the issue. Validating each measurement model separately before integrating them into the full structural framework ultimately improved the stability of the model.
Hey there! I’m really intrigued by your SEM model - it sounds super complex and interesting! 
Have you considered the sample size you’re working with? Sometimes these Hessian issues pop up when we don’t have enough data points compared to the number of parameters we’re estimating. How many observations do you have?
Another thing to think about - are all your constructs really distinct from each other? I’ve had issues before where highly correlated factors caused similar warnings. Maybe try running a correlation analysis on your variables to see if any are too closely related?
Oh, and have you looked into bootstrapping? That can sometimes help with tricky models like this.
What software are you using btw? I’m always curious to hear what tools other researchers are working with for SEM stuff.
Keep us posted on how it goes! SEM can be a real headache sometimes but so rewarding when you finally crack it. 
hey, ive run into similar issues b4. sounds like ur model might be overparameterized or have identification probs. try simplifying it by removing some paths or combining subconstructs. also, check for multicollinearity between variables. if that doesnt work, u could try a different estimation method like MLR. good luck!