Hi, I am running a VERY simple path model with repeated measures over time (math scores at T1, T2, T3). I want earlier math scores to predict later math scores but I also want to account for the autoregressive nature of it. I was under the impression that I cannot have the same variables in an ON statement and a WITH statement. Therefore, how do I account for the correlation of the errors between Y1, Y2, and Y3 in the following model?
I'm doing a path model with 2 forms of aggression (Y1 and Y2) on 8 independent variables (X1-X8). I need to control for the high correlation between Y1 and Y2. Will the command "Y1 with Y2;" control for shared variance or will it only report error correlation ?
I also tried Y1 on Y2; Y2 on Y1; But I have a just-identified model.
My question : How can I control for the shared variance between Y1 and Y2 ?
Dear Prof. Bengt and Linda, I have a similar question with above post. I do SEM and my IV is OT, DVs are V and S. My model structure is as follows: MODEL: OT BY ot7 ot3 ot2; RC BY rc3 rc6 rc8; TC BY tc2 tc3 tc4; PCBR BY pcb5 pcb4 pcb1; VO BY v5 v2 v1; SI BY s4 s2 s1; RC ON OT; TC ON OT; PCBR ON RC TC; SI ON TC PCBR OT; VO ON RC PCBR OT; SI ON sex age otenure ft; VO ON sex age otenure ft; I want control correlations between RC and TC, and between VO and SI. Should I write like as follows? RC TC; [RC TC]; RC WITH TC; VO SI; [VO SI]; VO WITH SI; Or write directly as RC WITH TC; VO WITH SI; Thank you very much and I look forward to your reply.
Paula Vagos posted on Monday, August 24, 2015 - 9:00 pm
Dear Professor Bengt, I am testing for multi-group invariance using the MLR estimator and get modification indices suggesting WITH mofications. I take they represent correlation between residuals, and I was wondering if the way to put them into a mplus syntax is simply u1 with u2 for example, or if this only applies to ML. I have tried doing this and get what I think is a correlation value between the two observed variables in the ouput, though the model fit improves.
Thank you for your reply. My kindest regards, Paula Vagos