I’m having trouble with a Structural Equation Model (SEM) using the sem package. When I run the model, I get this warning:
Warning message:
In eval(expr, envir, enclos) :
Could not compute QR decomposition of Hessian.
Optimization probably did not converge.
When I try to get a summary, I see this error:
Error in summary.objectiveML(cfa1.test) :
coefficient covariances cannot be computed
In addition: Warning message:
In vcov.sem(object, robust = robust, analytic = analytic.se) :
singular Hessian: model is probably underidentified.
My model looks at Market Orientation, Government Incentives, Entrepreneurial Orientation, and Firm Performance. Each of these has sub-constructs with observed variables. I’ve set up the model with specifyModel(), including lambdas for observed-to-latent relationships, paths between constructs, and error terms.
What do these messages mean? How can I fix this? Any advice would be really helpful!
yo, ClimbingMountain! those errors are a pain. ive seen this before - ur model might be too complex. try simplifying it, maybe cut down on some constructs or combine em. also, hows ur sample size? if its too small, that cud be the issue. n dont forget to check for multicollinearity! lemme know if u need more help, k?
I’ve encountered similar issues in my SEM analyses. The warnings indicate that your model may be underidentified or experiencing convergence difficulties, often because it is too complex for the available data. From my experience, simplifying the model by reducing the number of parameters or merging some constructs can help resolve these issues. Additionally, increasing the sample size might improve the model’s performance. It’s also important to check for multicollinearity among the variables and to examine the measurement model separately before moving to the structural part.
Considering modification indices can reveal potential misspecifications, and if standard adjustments do not resolve the problem, alternative estimation methods such as robust maximum likelihood or bootstrapping might be useful. Lastly, verifying the data for outliers or missing values is a good practice, as model specification is often an iterative process that requires ongoing adjustments.
Hey there, ClimbingMountain! 
Whoa, those error messages sound pretty frustrating! I’ve run into similar issues with SEM before, and it can be a real head-scratcher. Have you considered that your model might be a bit too complex? Sometimes when we’re dealing with multiple constructs and sub-constructs, it’s easy to accidentally create an overidentified model.
Just brainstorming here, but maybe you could try simplifying your model a bit? Like, what if you focused on just two or three main constructs to start with? Or maybe combine some of your sub-constructs?
Also, I’m curious - how large is your sample size? Sometimes these issues pop up when we don’t have enough data to support all the parameters we’re trying to estimate.
Oh, and have you checked for any multicollinearity between your variables? That can sometimes throw a wrench in the works too.
What do you think? Have you tried any of these approaches yet? I’d love to hear more about your model and data if you’re up for sharing!