CODEWITHSUNDEEP
rr-lavaan
Orlando Sabogal
Orlando Sabogal

Reputation: 1638

lavaan - problems with WLSMV estimator

I am running a SEM using lavaan that includes 5 latent variables. Also, I have 5 regression equations (Y~...) where outcomes are manifest variables and regressors are a mix of latents and indicators.

When I use maximum likelihood estimation the model runs without problem. But when I switch to WLSMV estimation (adding the argument estimator = "WLSMV") I am finding two problems. The first problem is that the execution becomes extremely slow taking several hours to run a single model, any idea why this is happening and if there is a way to fix it?

The second problem is that when I try to fit multigroup SEMs and start constraining the model I get the following warning:

lavaan WARNING: the optimizer (NLMINB) claimed the model converged,
                but not all elements of the gradient are (near) zero;
                the optimizer may not have found a local solution
                use check.gradient = FALSE to skip this check.

Any idea what this means? what are the implications? is this a problem? how I fix it? should I simply stay with maximum likelihood?

IMPORTANT: when I remove the regressions and keep only the measurement part (the five latent variables) the function execute fast and I stop getting the warning message. Does it mean that WLSMV should not be used when the CFA becomes a SEM?

Thanks in advance!

Upvotes: 0

Views: 1603

Answers (1)

Sinval
Sinval

Reputation: 1417

You have a big model for a small sample, I bet, and particularly small for the DWLS estimator with a mean- and variance-adjusted (MV) chi-squared test statistic... WLSMV. You can try to simplify your model, increase your sample, or use a different estimator, like the "MLR" maximum likelihood estimation with robust (Huber– White) standard errors.

I suggest that you check the chapter by Finney, DiStefano, and Kopp (2016).

S.J. Finney, C. DiStefano, J.P. Kopp. Overview of estimation methods and preconditions for their application with structural equation modeling K. Schweizer, C. DiStefano (Eds.), Principles and methods of test construction: Standards and recent advances, Hogrefe Publishing, Boston, MA, USA (2016), pp. 135-165, 10.1027/00449-000

Upvotes: 1

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