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?
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?
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).
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.