Path analysis for reciprocal relation... PreviousNext
Mplus Discussion > Structural Equation Modeling >
 ecl posted on Wednesday, February 04, 2009 - 12:15 pm
I have two observed variables measured at 4 time points. I want to use an auto-regressive cross-lag model with contemporaneous effects to investigate the reciprocal relationship between these variables. Can you suggest any examples for how to do this using Mplus?
 Linda K. Muthen posted on Wednesday, February 04, 2009 - 3:45 pm
You would use the ON option to specify this model, for example,

y4 ON y3; y3 ON y2; y2 ON y1;
z4 ON z3; z3 ON z2; z2 ON z1;

y4 ON z3;
z4 ON y3;

y2 ON z2;
z2 ON y2;

I'm not sure exactly how you want to specify your model but those are the basic building blocks. I'm not sure about identification.
 ecl posted on Friday, February 13, 2009 - 3:13 pm
To build upon my past question, I essentially want to have 2 repeated measures models with random intercepts for the 2 variables measured across the 4 time points(i.e. a repeated measures model for y1 y2 y3 y4 and a repeated measures model for z1 z2 z3 z4). I also want to include cross-lagged paths between the variables (i.e. y2 on z1, y3 on z2, y4 on z3, z2 on y1, z3 on y2, z4 on y3) and correlation between the random intercepts. Will this work with Mplus, and if so, how would I go about specifying it?
 Linda K. Muthen posted on Sunday, February 15, 2009 - 10:52 am
It sounds like you want a hybrid model. Start with Example 6.1 and add the ON statements that you want.
 Natalie posted on Tuesday, February 23, 2010 - 2:52 pm

I have a similar model to the one described above. It has two repeated measures that were collected at three time points. I've presented a model with cross-lagged paths between T1 and T2 and between T2 and T3, as well as the auto-regressive paths from T1 to T2 and T2 to T3. A reviewer would like me to add in autoregressive paths for both variables from T1 to T3, as well as cross lagged paths from T1 to T3. Is there any reason I would want to do this? Note that my measures are at least moderately stable across time (standardized betas > .55 for each auto-regressive path).

Thank you!
 Linda K. Muthen posted on Tuesday, February 23, 2010 - 4:24 pm
I don't know if there is any reason but you might add the paths to the model and see what happens. Perhaps some evidence for an argument one way or the other will show up.
 Jahun Kim posted on Wednesday, June 22, 2011 - 9:58 pm
Dear Dr. Muthen,

I have two (observed) variables measured two time points. I'd like to examine reciprocal relationship between these two variables with cross-lagged model.
I build up my model based on your posted answer this page. In the output, Chi-square is 0, RMSEA is 0, and CFI is 1.00.

Could you take a look at my input?




q3p_dep on q1p_dep;
q4psupp on q2psupp;

q4psupp on q1p_dep;
q3p_dep on q2psupp;
 Linda K. Muthen posted on Thursday, June 23, 2011 - 11:30 am
This looks correct. The model has no degrees of freedom. Model fit cannot be assessed.
 Jahun Kim posted on Thursday, June 23, 2011 - 11:36 am
Thank you Linda.
 Eric Thibodeau posted on Sunday, March 08, 2015 - 4:17 pm

I'm trying to run a cross sectional SEM with a reciprocal paths between two observed variables.

For example:

f1 by in1 in2 in3 in4;

f1on X Y M;
X on Y;
Y on X M;

Do I need to specify a covariance between X and Y disturbances? If so how do I do that? Thanks!
 Linda K. Muthen posted on Sunday, March 08, 2015 - 5:35 pm
In a reciprocal interaction, each dependent variable must have a unique predictor. Y has m but x does not have a unique predictor.
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