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 Christian S posted on Wednesday, August 25, 2010 - 11:51 am
Dear Drs. Muthen,

I have a CFA with latent factors (each measured with multiple Likert-scale [-3;3] indicators). For the descriptive statistics, I would like to list mean and std. deviation for each latent factor. The TECH 4 output gives me 0 as mean for every factor. What should I do to get the "real" means and the standard deviations? Does the fact that I get 0 as mean for each factor mean that something with my model is wrong?

I really appreciate your reply.

Best regards,

Christian
 Linda K. Muthen posted on Wednesday, August 25, 2010 - 2:44 pm
In cross-sectional studies, the means of latent variables are zero. In multiple group analysis or with repeated measures, the means of latent variables are zero in one group or at one time point and are estimated in the other groups or time points.
 Christian S posted on Wednesday, August 25, 2010 - 3:21 pm
Dear Dr. Muthen,

thank you very much for your reply. As far as I understand, you wrote how Mplus handles factors in cross-sectional studies.

However, in many publications, I find means and std. dev.s of latent factors in the descriptive statistics part.

When indicators are e.g. all skewed to the right (avg>0), then the mean of the latent variable should (e.g. in my example with Likert scales from -3 to 3) not be zero. Is there a way with Mplus to get this mean? Should I use an average of the indicators of each factor weighted by the indicators' yx-standardized factor loading and then calculate the mean and the std. dev. for each factor?

Best Regards,

Christian
 Linda K. Muthen posted on Wednesday, August 25, 2010 - 3:38 pm
This is not how Mplus handles factors in cross-sectional studies, it is the conventional way to do this. A factor mean in a cross-sectional study has no meaning. You can't compare it to other factor means because there is no basis for comparison. It makes sense to compare factor means only across groups or across time after measurement invariance has been established.

I would imagine in the studies you mention, factor score means are being reported. Factor scores are generally not good approximations of true factor values.

The mean of a factor indicator is equal to the intercept of the factor indicator plus the factor loading times the mean of the factor. When the factor mean is zero, the mean of the factor indicator is equal to its intercept. This is why the factor mean can be zero even when the observed variable indicator mean is not zero.
 Brewery Lin posted on Friday, April 06, 2012 - 12:12 am
In Byrne's book (2012), p.211
Appearing below these specifications, however, you will see the following:
[Fl@O F2@0 F3@0]......in structuring the input file for a configural model, it is necessary to void this default by fixing all factor means to zero.

Is it more appropriate to do that constriain? Thank you.
 Linda K. Muthen posted on Friday, April 06, 2012 - 8:27 am
To test if factor means are different across groups use a model where factor means are zero in all groups versus a model where factor means are zero in one group and free in the other groups.
 Brewery Lin posted on Sunday, April 08, 2012 - 7:42 pm
Dear Linda,
If I just want to test the configural model, is it still recommend to do that?

Thank you.
 Linda K. Muthen posted on Monday, April 09, 2012 - 8:21 am
The configural model is factor means free across groups, intercepts free across groups, and factor means zero in all groups. You may find the multiple group section of the Topic 1 course handout on the website useful. It shows all of the inputs needed to test for measurement invariance.
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