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
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?
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;
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
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?
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
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:
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)?
Hello, I have a diary study with 2 measurement occasions per day over 5 days. I would like to test cross-lagged effects WITHIN days (thus the effect of Variable A at time 1 on Variable B at time 2 controlling for B at time 1). I would like to model this simultaneously at the within- and between-person level. Since I have only 2 measurement occasions per day, I test it with type is twolevel rather than with a latent growth model.
ANALYSIS: TYPE IS TWOLEVEL;
MODEL: %WITHIN% Bt2 ON At1; Bt2 ON Bt1; At1 WITH Bt1;
%BETWEEN% Bt2 ON At1; Bt2 ON Bt1; At1 WITH Bt1;
My question is whether it makes sense to control for Bt1 at the between-person level. I am in doubt because the sign of the effect of At1 on Bt2 changes when I control for Bt1 and this seems to be due to a suppressor effect.
Margarita posted on Wednesday, October 19, 2016 - 4:42 am
Dear Dr. Muthen,
I am testing a 3x3 (9 variables) cross-lag model with (6) latent variables and WLSMV estimator. I'd like to account for the clustering in my data (schools) but also compare nested models. My understanding is that DIFFTEST is not available with Type = twolevel, so I am not sure how to approach this. Should I
1) use WLSM instead? 2) use the Satorra-Bentler test or 2) use type = complex that allows for DIFFTEST? All variables used in the model were assessed on the individual level (within), so I have no variables in the between level. Would complex be enough to account for the clustering in this case?
I assume that your latent variable indicators are categorical. Note that you can use ML or Bayes. With ML you can use likelihood-ratio chi-2 testing of nested models. Twolevel WLSMV does not come with Difftest.
Margarita posted on Thursday, October 20, 2016 - 1:11 am
Thank you for your response.
Basically I am trying to run a multigroup (gender) multilevel (to account for the clustering -schools) cross lagged model with 6 latent factors with categorical indicators (loadings held constant across the three time points) , and I also want to compare nested models using the chi-square statistic. Is it possible to account for all of this in a model?
I did not use ML because I have 6 latent factors ( 5 categorical indicators in each) which makes it very computationally heavy. I read that likelihood ratio chi-square under integration = montecarlo might be imprecise. I tried using ML with integration = standard (7) but I wasn't sure if it was the right approach and it was taking long to run. Any suggestions would be greatly appreciated!
I would like to run a twolevel cross-lagged analysis where x and y were measured 3 times a day for 7 days for 140 people. My data are in long format and time of day is coded 0, 1, 2. I was wondering if there is away to examine only the lags within the day (time0 to time1, and time1 to time2) and not include lags across days (time2 on day 1 to time0 on day 2)?
You can set it up as a multivariate model y1 on y2; y2 on y3; It will be a two-level model with cluster=id and 7 obs in each cluster, there won't be further time-series in it. You can take a look at Part 6, page 48-53 http://statmodel.com/download/Aug17-18_JH_Slides.zip but your example is much simpler really.
Kelly Harper posted on Saturday, February 10, 2018 - 9:13 am
Thank you. That is helpful.
Just to clarify, it seems like I cannot use the lagged command with my data and have to convert the data to wide format with variables for the specific time points?
This is what my suggestion above is, however, on a second thought I would instead recommend that you don't do that and instead used the lagged command. If you have the precise times of measurements use those, if not, you can use something like that, 0,1,2 (for day 1), 6,7,8 (for day two) leaving the periods 3, 4, 5 to represent night, etc ... When you have this kind of setup, the correlation across day will be minimal due to the extended distance between the times of observations. You can choose two periods instead of 3 to represent the night interval or whatever makes the most sense.