[R] Results of CFA with Lavaan

[R Help]

Thanks for the help, the std.lv=TRUE command is exactly what I was
looking for.  As you stated, it doesn't matter in terms of overall
model fit, but my client is more interested in the loadings than the
factor variances.

In terms of speed, it's just a very large model (7 factors, 90
observations, only ~560 subjects) with missing values, so I don't
expect much in terms of speed.  I think the overall conclusion for the
project is that the model is poorly specified, but whether that's the
model itself or the lack of samples is difficult to determine at this
point.

Thanks for your help, and I'll certainly be using lavaan in the future,
Sam

On Thu, Jun 9, 2011 at 6:19 AM, yrosseel <yrosseel at gmail.com> wrote:
> On 06/08/2011 11:56 PM, R Help wrote:
>>
>> Yes, that is the difference.  For the last SEM I built I fixed the
>> factor variances to 1, and I think that's what I want to do for the
>> CFA I'm doing now.  Does that make sense for a CFA?
>
> If you have a latent variable in your model (like a factor in CFA), you need
> to define its metric/scale. There are typically two ways to do this: 1) fix
> the variance of the latent variable to a constant (typically 1.0), or 2) fix
> the factor loading of one of the indicators of the factor (again to 1.0).
> For CFA with a single group, it should not matter which method you choose.
> The fit measures will be identical.
>
> Lavaan by default uses the second option. If you prefer the first (fixing
> the variances), you can simply add the 'std.lv=TRUE' option to the cfa()
> call, and lavaan will take care of the rest.
>
>> I'll try figuring out how to do that with lavaan later, but my model
>> takes so long to fit that I can't try it right now.
>
> You can use the 'verbose=TRUE' argument to monitor progress. You may also
> use the options se="none" (no standard errors) and test="none" (no test
> statistic) to speed things up, if you are still constructing your model. Or
> the model does not convergence, but I should see both the model and the data
> to determine the possible cause.
>
> Hope this helps,
>
> Yves Rosseel
> http://lavaan.org
>
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