Cross-lagged multilevel model? PreviousNext
Mplus Discussion > Structural Equation Modeling >
Message/Author
 WAM posted on Wednesday, November 23, 2011 - 6:47 am
Hello,
I tested a reciprocal relationship between two variables (measured at 3 occasions). One of the reviewers insists that the cross-lag model must be tested using M+ by taking the multi-level structure of the data (occasions nested within students, which are nested within classrooms) in to account. Is there such a possibility?
Thanks,
Wond
 Linda K. Muthen posted on Wednesday, November 23, 2011 - 1:37 pm
Yes, see Example 9.12.
 Natalie Wright posted on Monday, April 21, 2014 - 4:42 pm
Hi,
I have a question similar to the one asked by the previous poster. I want to test an autoregressive latent trajectory model incorporating 2 variables measured on 3 occasions. However, the data has a nested structure (students within classrooms). The higher-order units aren't of substantial research interest. I know that multilevel latent growth models and multilevel cross-lagged models can be tested in Mplus, but I haven't seen anything, either in the Mplus documentation or elsewhere, regarding multilevel ALT models. Can this model be specified in Mplus? If so, would TYPE=TWOLEVEL or TYPE=COMPLEX option be more appropriate?

Thanks for any help that you can provide!
 Linda K. Muthen posted on Tuesday, April 22, 2014 - 10:07 am
Mplus can estimate multilevel ALT models. I don't have an example. You might want to ask if someone on SEMNET has one. I think TYPE=TWOLEVEL would be the best choice.
 Wonho Jeung posted on Sunday, May 17, 2015 - 9:46 am
Hello,
I am testing a reciprocal and time-lagged relationship between two variables (both variables are time-varying measured at 3 occasions). The data has a multilevel structure such that occasions for both variables are nested in person and person nested in teams. You mentioned to see Example 9.12 but I don't know how to apply that example to my model. It has only one time-varying variables.

The following is how I enterd the model.


VARIABLE: NAMES ARE v001-v246;
USEVARIABLES ARE v001 v220 v221 v222 v244 v245 v246 ;
Missing are all (999);
Cluster is v001;

ANALYSIS:
TYPE IS Twolevel Random;
ALGORITHM=INTEGRATION;


MODEL:

%WITHIN%
iw1 sw1 | v220@0 v221@1 v222@2;
iw2 sw2 | v244@0 v245@1 v246@2;
iw2 sw2 on iw1;
iw1 sw1 on iw2;
iw1 with sw1;
iw2 with sw2;
iw1 with sw2;
iw2 with sw1;

%BETWEEN%
ib2 sb2 | v244@0 v245@1 v246@2;
v244-v246@0;


OUTPUT: TECH1 TECH8 CINTERVAL ;

Would you please tell me if it is appropriate?
 Bengt O. Muthen posted on Monday, May 18, 2015 - 12:28 pm
This looks ok assuming the 2 sets of outcomes are lagged in time. I don't understand why your Between level specification is for only 1 of the 2 processes.
 ZHANG Liang posted on Monday, August 10, 2015 - 8:12 pm
Hello,

If I'm doing a 2 time กม 2 variable (A and B) cross-lagged analysis, and trying to examine the effect of a BETWEEN level moderator (M) on one of the cross-lagged paths (A1 to B2), is it correct to write the syntax like below?

...
VARIABLE:
usevar = a1 a2 b1 b2 m;
within = a1 b1;
between = m;
cluster = cls;

MODEL:
%WITHIN%
s | b2 on a1;
b2 on b1;
a2 on a1 b1;

%BETWEEN%
s b2 on m;
...
------------------
And, is it recommended that to write the last line as "s on m;" instead of "s b2 on m;", when I'm not interested in how the level 2 variable M predict B2?

Thank you in advance!
 Bengt O. Muthen posted on Tuesday, August 11, 2015 - 1:37 pm
Q1. Yes.

Q2. That's fine. Just make sure you estimate the variance of b2 on between (and perhaps covariance with s).
 Ai Ye posted on Wednesday, August 12, 2015 - 1:00 pm
Hello,

I tested a bidirectional relationship between two mathematical learning measures (measured at five time points) using a cross-lagged panel model. Correlation analysis reveal a positive relationships between the two measures across time. I used the following codes:

MODEL:

dcas5f with con5f ;
dcas4f with con4f ;
dcas3f with con3f ;
dcas2f with con2f ;
dcas1f with con1f ;

dcas5f on dcas4f con4f ;
dcas4f on dcas3f con3f ;
dcas3f on dcas2f con2f ;
dcas2f on dcas1f con1f ;

con5f on con4f dcas4f ;
con4f on con3f dcas3f ;
con3f on con2f dcas2f ;
con2f on con1f dcas1f ;

However, there is one coefficient (con3f predicting dcas4f) that is (unexpectedly) negatively significant. I would like to ask under what circumstance two positively correlated measures could be negatively related in a cross-lagged model and how should I interpret it (as I know that conceptually a higher score in one math measure leads to a higher score in the other math measure)?

Thank you very much!
 Bengt O. Muthen posted on Wednesday, August 12, 2015 - 5:15 pm
You may want to direct this general modeling question to SEMNET.
Back to top
Add Your Message Here
Post:
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Password:
Options: Enable HTML code in message
Automatically activate URLs in message
Action: