Hello Dr. Muthen, I modeled a bivariate autoregressive model. Looking at the standardized solution some estimates are bigger than 1. Normaly this could indicate multicollinearity. I can't find any information if this is as problematic as in normal regressions or if this is formed through the autoregressive process. Should I transform the data? Or isn't it problematic in those kind of models? Could you please help me? Thanks in advance. Kind regards, Jana
Dear Mr. Muthen, as expected the estimated correlation among the variables with autoregressive relationship are very high (around 0.99). Your assumtion regarding the allowed residual correlations for variables at the same time point is right but i also tried to estimate the model with fixed residual correlations to 0 - without any changes. Should I try to find a solution without those problems? Or is it possible to report such a model with standardized estimates larger than 1? I found a text of joereskog about this problematic, but it only mentioned the same thing I already knew: higher values might suggest that there is a high degree of multicollinearity in the data. I didnt find any papers dealing with that problem within those autoregressive path models. Kind regards, Jana
I'm currently trying to run a pathway analysis model with the following variables:
IV: X1 (experimentally manipulated between-subject variable with two levels)
Mediators: Y1, Y2, Y3, Y4 (all continuous variables measured after the manipulation of X1)
DV: Y5 (continuous variable measured after the mediators)
I set up the following model:
Y1 ON X1 Y2 ON X1 Y3 ON X1 Y4 ON X1 Y5 ON Y1 Y2 Y3 Y4 X1 Y1 WITH Y2 Y3 Y4 Y2 WITH Y3 Y4 Y3 WITH Y4
Y5 IND Y1 X1 Y5 IND Y2 X1 Y5 IND Y3 X1 Y5 IND Y4 X1 Y5 IND X1
I'm concerned that I might be having a multicollinearity issue with my four mediating factors but I'm unsure how to test for multicollinearity in Mplus. I had a look through the manual and online but I have been able to find anything that helps me.
Hello Prof. Muthen, I have got a question: I modeled a bivariate autoregressive model. Yet, looking at the standardized solution (stdyx)I found that some estimates are bigger than 1. At first I thought it might be due to multicolinearity, so I did what you suggested above and had a look at the correlations using TECH4. However, the correlations between my latent variables vary between .12 and .38 so I don`t think that multicolinearity is relevant here. Do you have any idea why I got coefficients bigger than 1?