Incorrect Covariance Coverage PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
Message/Author
 Diana Rancourt posted on Monday, September 20, 2010 - 6:32 pm
I've restructured longitudinal data from 3 waves of repeated measures to 5 grade repeated measures as per Bollen and Curran's (2006) synthetic cohort approach. I have been successful using this approach with two separate outcome variables so far, but am having trouble with a third separate outcome variable. When I try to run the model MPlus is detecting data where there should not be any (i.e., where the covariance coverage should be zero). I have checked the data set and tried recreating the .csv data file from scratch in case something went wrong in the conversion process. None of this seems to have worked, however, and I was wondering what else might be going wrong that the covariance coverage is incorrect. Any help would be much appreciated -- thank you for your time!
 Linda K. Muthen posted on Monday, September 20, 2010 - 8:36 pm
Please send the files and your license number to support@statmodel.com.
 Chong M. Chow posted on Wednesday, June 01, 2011 - 5:24 pm
Dear Linda,

I have a question regarding obtaining a negative variance in my growth factor.

I am studying the change of kids' diabetes adherence overtime. When i fitted a univariate growth model for the repeated measures, the model fit was fine and there is no negative variance for the growth factor. However, once i include other predictors in my model, i obtained negative variance for the growth factor. I wonder if this could be due to the fact that the is lack of variability in the diabetes adherence growth factor?

Thank you for your time.

Chong.
 Owis Eilayyan posted on Thursday, April 26, 2012 - 2:26 pm
Hello,

I run a path analysis model and all Covariance Coverage values are more than 0.85.
does that mean i have a multi-collinearity?

Thanks
Owis
 Linda K. Muthen posted on Thursday, April 26, 2012 - 7:47 pm
No. The coverage values tell you the percentage of people with non-missing values on the variable or pair of variables.
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