Unable to Perform QR Decomposition of Hessian in CFA/SEM Model

CFA
1

I am using the sem package to create a SEM model. Though the model runs, I get a warning:

Warning message:
In eval(expr, envir, enclos) :
Could not compute QR decomposition of Hessian.
Optimization probably did not converge.

When I call summary(model_test), it returns:

Error in summary.objectiveML(model_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 SEM model examines relationships between Market Orientation (MO), Government Incentives (GovInc), Entrepreneurial Orientation (EO), and Firm Performance (FirmPerf), with various constructs under each. Here’s the simplified code:

model_def <- specifyModel()
 GovInfo -> GovInfo1, NA, 1  
 GovInfo -> GovInfo2, lamd1, NA
 GovInfo -> GovInfo3, lamd2, NA
...
 GovInfo -> GovInfo7, lamd6, NA
...
 Perf -> Econ1, NA, 1
...
 Perf -> Econ12, lamd23, NA
...
MO -> GovInfo, NA, 1
...
 EO <-> FirmPerf, psi1, NA
...

cfa_test <- sem(model_def, data_matrix, N = 500)
summary(cfa_test)

I need guidance on understanding these errors and warnings, and how to address them. Your input would be very helpful.

2

It sounds like you’re dealing with a tricky model identification problem. These issues often crop up in complex SEM models, especially when you’ve got a lot of parameters to estimate relative to your sample size.

First, I’d suggest double-checking your model specification. Make sure you haven’t accidentally left any paths unconstrained or overspecified parts of the model. It’s easy to miss something when you’re dealing with multiple latent variables and indicators.

If that doesn’t solve it, you might need to simplify your model. Maybe try removing some of the less critical paths or combining some indicators. Sometimes, less is more in SEM.

Another approach is to increase your sample size if possible. A larger N can help with model stability and identification.

Lastly, consider using a different SEM package like lavaan or Mplus. They sometimes handle these issues better and offer more diagnostic tools.

Don’t get discouraged – SEM can be finicky, but with some tweaking, you’ll likely get it sorted out. Good luck with your analysis!

3

Hey there! :wave: Those SEM errors can be a real head-scratcher, huh? I’ve been there too, and it’s like trying to solve a puzzle with missing pieces sometimes.

Have you thought about playing around with your model specification a bit? Sometimes, it’s just a matter of tweaking things here and there. Maybe try removing a few paths and see if that helps? Or you could try adding some constraints if you haven’t already.

I’m curious - how big is your sample size? Sometimes these issues pop up when we’re working with smaller datasets. If you’ve got the option to collect more data, that might help stabilize things.

Oh, and have you considered giving lavaan a shot? It’s pretty user-friendly and might handle these hiccups better. Plus, it’s got some neat diagnostics that could give you more insight into what’s going on under the hood.

What do you think might be causing these issues in your specific model? I’d love to hear more about your research - it sounds really interesting! Maybe we could brainstorm some more ideas together?

4

hey, those errors can be frustrating! sounds like you might have an identification problem. have u tried simplifying ur model? maybe remove some paths or combine indicators. also, check if ur sample size is big enough for all those parameters. lavaan could be worth a shot too - it’s pretty good with tricky models. don’t give up, you’ll figure it out!