Correlated slopes PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 JM posted on Monday, September 07, 2009 - 2:35 pm
Hi quick clarification question, if two slopes are both negative, and positively correlate in a longitudinal parallel-process model, what exactly does that mean....both slopes are negative but positively correlate? Thanks!
 Linda K. Muthen posted on Tuesday, September 08, 2009 - 9:59 am
When one is lower the other is lower. When one is higher the other is higher.
 jpmv posted on Monday, April 18, 2011 - 2:08 pm
I am interested in the correlation between the slopes of two variables. Because I was wondering whether the results would be the same when shared variance between the two variables were removed, I added the within time-correlations at each time point in the model specification. I have three questions hereby:

1. Was this the correct way to control for shared variance?
2. What are the pro's and contra's of controlling for shared variance?
3. The correlation between the slopes remains similar, but this result is no longer significant (despite the strong negative correlation). How should I interpret this?

Thank you!
 Linda K. Muthen posted on Tuesday, April 19, 2011 - 9:32 am
It soundslike you are talking about the residual covariance at the same time point between two outcomes that are part of two different growth model. Is this correct? Is this residual covariance what you mean by shared variance?
 jpmv posted on Wednesday, April 20, 2011 - 6:07 am
Indeed, this is what I mean.
 Bengt O. Muthen posted on Thursday, April 21, 2011 - 8:06 am
I think allowing for contemporaneous residual covariance between outcomes of two different growth processes is often necessary to capture the effects of left-out time-varying covariates that influence both processes. This then avoids channeling too much of the correlation between the two outcomes through the growth factors. That is, the correlation you get between the growth factors is more trustworthy when you include these contemporaneous residual covariances.
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