When there is high (almost perfect) correlation between intercept and slope in growith curve model, the model fails to run or gives improper solution. When I ran the model with random options and analysis it failed to run with saying "nonpositive defninite estimated correlation matrix". I guess this is the natural reaction of mplus because intercept or slope factor has negative or 0 residual variance if the correlation between them is close to 1(or -1). Is there any remedy for this problem?
I have a similar question. My slope and intercept are highly correlated (more than .80). So far I have done it so, that the loading of the variable at Time 1 is set to 0). However, when I center so that intercept is the last time point, the correlation is even higher. It would not be such a problem, but I am trying to examine whether individual differences in the slope and intercept predict differences in other variables. So...what would be the solution? Should I try to do growth mixture analysis? Thank you.
I forgot to add...the problem is that when I include both the intercept and slope in the model, all the paths are nonsignificant. However, the paths from the intercept become significant when I fix the paths from the slope to be zero (and the other way around).