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 Renata Estrella posted on Friday, December 09, 2005 - 1:41 pm
Hello everybody,

I'd like to know what type of estimation should I use if I have a model with sampling weights?
 Linda K. Muthen posted on Friday, December 09, 2005 - 2:03 pm
It's not as estimation choice. You would use the WEIGHT option of the VARIABLE command so that estimation would take into account sampling weights.
 Renata Estrella posted on Friday, December 09, 2005 - 2:06 pm
Hi Linda,

Thanks...
I did that you told me but I got the following message:
*** ERROR in Analysis command
Sampling weights is not available for estimator ML.

Can you help me?

Thanks again
 Linda K. Muthen posted on Friday, December 09, 2005 - 2:21 pm
Sampling weights are available for MLR, MLM, and MLMV. I would suggest MLR.
 Renata Estrella posted on Friday, December 09, 2005 - 2:59 pm
Thanks a lot...

Can you suggest some reading about these methods: MLR, MLM and MLMV?

Thanks again
 Linda K. Muthen posted on Friday, December 09, 2005 - 3:41 pm
The user's guide has brief descriptions. Look under ESTIMATOR in the index. Also Technical Appendix 4 which can be accessed from the website.
 Katayoun Safi posted on Wednesday, November 04, 2009 - 12:07 pm
Hi,
I receive the following message, when I try to use my sampling weight: "NO CONVERGENCE. NUMBER OF ITERATIONS EXCEEDED", while there is no problem in estimating the same model without weights (I use MLR as estimator for the weighted estimation). How can I solve this problem? Thank you very much.
 Linda K. Muthen posted on Wednesday, November 04, 2009 - 12:53 pm
Please send the input, data, output, and license number to support@statmodel.com.
 Lisa M. Yarnell posted on Tuesday, February 08, 2011 - 6:55 pm
Hello, I applied sampling weights for a latent variable SEM model estimated with WLSMV estimation. (Because I have many dichotomous indicators, WLSMV was recommended by a professor.)

I ran one of my models and it had 139 degrees of freedom. When I applied sampling weights, without otherwise changing the model, the model had 91 degrees of freedom.

It it typical to lose degrees of freedom simply by applying sampling weights? Why?

Thank you,
Lisa
 Linda K. Muthen posted on Tuesday, February 08, 2011 - 7:08 pm
It seems you are using a version of Mplus prior to Version 6. The degrees of freedom for WLSMV were not computed in the regular way. You should use WLS or WLSM to see the expected degrees of freedom for your model. Prior to Version 6, the chi-square and degrees of freedom were adjusted to obtain a correct p-value and only this p-value was interpretable.
 Lisa M. Yarnell posted on Wednesday, April 13, 2011 - 8:44 pm
Dr. Muthen,

Because I have many dichotomous indicators, I have been using WLSMV estimation. You mentioned that with versions of Mplus prior to Version 6, the Chi-Square values and degrees of freedom in the output are not correct, and that one can get the appropriate degrees of freedom from a model estimated with WLS.

Should one get the Chi-Square fit statistic from a WLS-estimated model as well? (I think the fit would be different if I used WLS instead of WLSMV, though, because I am working with several dichotomous indicators, and my matrices do break assumptions of multivariate normality.)

Also, are the Chi-Square statistics for the DIFFTEST command (used for Chi-Square fit comparisons when one is using WLSMV) accurate with versions of Mplus prior to Version 6? I ran DIFFTEST several times in my work, but I am using Version 5.2, and I wonder if the results from the DIFFTESTs that I am reporting are accurate.

Thank you.
 Linda K. Muthen posted on Wednesday, April 13, 2011 - 8:59 pm
Prior to Version 6, chi-square and degrees of freedom were adjusted to obtain a correct p-value. This is what should be reported. You should not report a chi-square from a different estimator.

DIFFTEST is fine in Version 5.2.
 Lisa M. Yarnell posted on Wednesday, April 13, 2011 - 9:16 pm
Thanks, Dr. Muthen. I am confused about the prior statement to "use WLS or WLSM to see the expected degrees of freedom for your model" if I am using a version prior to Version 6.

It seems that you are saying that the Chi-Square and degrees of freedom are fine for WLSMV-estimated models with Version 5.2. Isn't this inconsistent with the first statement?

Thanks again for clarifying this for me.
 Linda K. Muthen posted on Thursday, April 14, 2011 - 2:52 pm
In Version 5.2, you should report only the p-value for the chi-square when WLSMV is used for the reasons stated above. If you want the degrees of freedom calculated in the regular way, you will find them if you use WLS or WLSM or you can calculate them yourself.
 Lisa M. Yarnell posted on Thursday, April 14, 2011 - 4:35 pm
OK, I wonder if I can report the degrees of freedom from my output (using WLSMV in Version 5.2) and simply state that these are adjusted degrees of freedom?
 Linda K. Muthen posted on Thursday, April 14, 2011 - 8:31 pm
This would need to be your decision.
 Lisa M. Yarnell posted on Thursday, April 14, 2011 - 8:42 pm
OK, thanks for your help!
 Cecily Na posted on Friday, June 01, 2012 - 5:09 pm
Hello, Linda
I think SEM with continuous outcomes assumes multivariate normality. How can I check multivariate normality if I use sampling weights?

Thanks
 Bengt O. Muthen posted on Friday, June 01, 2012 - 6:23 pm
Multiv non-normality is not a problem when you use the MLR estimator because it is non-normality robust.
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