Help with Bifactor Model

[akis...@mail.usf.edu]

Hello,

I am trying to run a bifactor model using the following syntax with the lavaan R package:

ThreeFactorBifNC <- 'G =~  ZTrailBlps + ZTrailAlps + ZUDSVERTN + ZDIGFORCT + ZDIGBACCT + ZCRAFTVRS + ZCRAFTDVR + ZUDSBENTD + ZVEG + ZANIMALS + ZMINTTOTS

VisExecAttn =~  ZTrailBlps + ZTrailAlps + ZUDSVERTN + ZDIGFORCT + ZDIGBACCT

Mem =~ ZCRAFTVRS + ZCRAFTDVR + ZUDSBENTD

Lang =~ ZVEG + ZANIMALS + ZMINTTOTS

G ~~ 0*VisExecAttn

G ~~ 0*Mem

G ~~ 0*Lang

VisExecAttn ~~ 0*Mem

VisExecAttn ~~ 0*Lang

Mem ~~ 0*Lang'

fitthreeBifNC <- cfa(ThreeFactorBifNC, data=Dataset2)

summary(fitthreeBifNC, fit.measures=TRUE, standardized=TRUE)

When I run the syntax, I get the following Warning: "lavaan WARNING: the optimizer warns that a solution has NOT been found!"

The output contains global model fit indices and standardized factor loadings, but no standard errors or p values.  Any suggestions on how to get the model to run correctly?

Of note, I was able to run the following models without receiving any warnings: A a unitary model, an orthogonal 3-factor model, and a higher order model (1 higher order factor and 3 lower order factors).

Thanks!

[Edward Rigdon]

Try allowing the group factors to covary among themselves.

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[Andrew Kiselica]

Thanks, Edward. That might work, but would be theoretically inadvisable, as the lower order factors should represent orthogonal latent traits

--

Andrew M. Kiselica, MA

University of South Florida

Doctoral Candidate in Clinical Psychology

[Edward Rigdon]

Andrew--

So factor correlations different from 0 would mean rejection of theory. But you need convergence before you can make that judgment 

[akis...@mail.usf.edu]

ThreeFactorBifNC <- 'G =~  ZTrailBlps + ZTrailAlps + ZUDSVERTN + ZDIGFORCT + ZDIGBACCT + ZCRAFTVRS + ZCRAFTDVR + ZUDSBENTD + ZVEG + ZANIMALS + ZMINTTOTS

VisExecAttn =~  ZTrailBlps + ZTrailAlps + ZUDSVERTN + ZDIGFORCT + ZDIGBACCT

Mem =~ ZCRAFTVRS + ZCRAFTDVR + ZUDSBENTD

Lang =~ ZVEG + ZANIMALS + ZMINTTOTS'

fitthreeBifNC <- cfa(ThreeFactorBifNC, data=Dataset2)

summary(fitthreeBifNC, fit.measures=TRUE, standardized=TRUE)

So I tried running the model removing the constraints, and received the following warnings:

"Warning messages:

1: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :

  lavaan WARNING:

    Could not compute standard errors! The information matrix could

    not be inverted. This may be a symptom that the model is not

    identified.

2: In lav_object_post_check(object) :

  lavaan WARNING: some estimated ov variances are negative"

Any further suggestions?

[Edward Rigdon]

What do the estimates look like? The model might be empirically underidentified--relxing the covariance constraints could actually have improved that situation, but maybe not enough. Two of your group factors have only 3 indicators apiece. If those factors are orthogonal to everything else, then ll of the loadings must be "large"--which will be more chalenging since the indicators all load on the general factor as well.

[Andrew Kiselica]

Ah, I see. Yes, some of the loadings are quite small <.01 for the  group factors. I'm guessing there's no fix for that issue?