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 - 3:00 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
I would appreciate your advice on the degree to which I should incorporate WITH commands in a moderately complex SEM.
A simplification of my current Model is (wave 1 variables generally are latent, wave 7 variables are generally binary indicator variables):
W7GH on W1GH W1Ability W4MH W1Fr (and controls) W7NEET on W1Ability W1GH W1MH W1Fr (and controls) W4MH on W1MH W1Ability W1GH W1Fr (and controls) W7Fr on W1Fr W1MH W1Ability W1GH (and controls)
W7Lifesat on W7GH W7NEET W4MH W7Fr (and controls)
For an additional command I have two options: 1) W7GH W7NEET W4MH W7Fr WITH W7GH W7NEET W4MH W7Fr 2) W7GH W7NEET W4MH W7Fr W7Lifesat WITH W7GH W7NEET W4MH W7Fr W7Lifesat
I have generally seen equivalents of option "1" used more often in the literature.
If I type in option "2" a) will MPlus allow the residuals from all the structural regressions to be correlated? b) what is your view on the legitimacy of this approach if I think there may be unexplained characteristics relating to the individuals which impact all the W7 variables?
When I do option "2" I get some quite odd results whereas the results when I do option 1 are quite intuitive.
I am looking to test how removing the effect of exogenous X3 affects the residual correlation in the following path model:
MODEL: Y1 ON X1 X2 X3; Y2 ON X1 X2 X3; Y1 WITH Y2; ! Residual correlation of interest
I suspect this can be done with MODEL CONSTRAINT and/or MODEL TEST but I am not sure how, and wanted to ask for suggestions.
I cannot just set X3@0 and then look at overall model differences, as the model difference test will reflect the regression paths being set to 0 in addition to a change in residual correlation. I am looking to test the difference in residual correlation only. Thank you!
I am trying to understand how estimating the residual covariances among endogenous mediators at the within person level impacts between level indirect effects. In the following model, the the estimates change depending on whether the covariances among residuals are estimated or not. Any info would be much appreciated.
%WITHIN% a1 | m1 on x; a2 | m2 on x; a3 | m3 on x; b1 | y on m1; b2 | y on m2; b3 | y on m3; x y m1 m2 m3 on t; y m1 m2 m3 on y&1;
m1 m2 m3 WITH m1 m2 m3; !covariance among residuals
%BETWEEN% y a1 a2 a3 b1 b2 b3 WITH y a1 a2 a3 b1 b2 b3;
a1 WITH b1 (cov1); a2 WITH b2 (cov2); a3 WITH b3 (cov3);
The estimates might be changing simply because you are using the Bayes estimation. With the Baysian estimation the estimates can change just a little bit because of the random nature of the estimation. Is the change in the estimates significant to warrant looking for another reason? Also, are m1-m3 centered and declared as within variables. If they are within-between variables the missing within level covariances will affect the between level covariance structure. Also, if you have missing data for m1-m3, the within level covariance structure will affect dramatically the missing value imputation and from there the rest of the model. I would recommend setting up a montecarlo simulation with large sample size as this will yield the best answer.
Thank you for the response. m1-m3 are centered and declared as within variables. The a paths are stable up to 2 decimal places with some variation in the 3rd decimal. The means and variances for the b paths are substantially different in some cases suggesting there might be a reason beyond the Bayes estimator. There is also a
c | y on x
I had forgotten to include that is very different across models. There are missing values on x, m1-m3, and y which I'm guessing from your response is the likely source. Thanks again.