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 emmanuel bofah posted on Wednesday, January 01, 2014 - 8:04 am
i have a three time point longitudinal data.All students in time point 1 are in time point 2 and and all students in time point 2 are in time point 3. but in time point 2 and 3 more students were included, so there are students in time point 2 and 3 not in time point 1 and students in time 3 not in time point 2 and 1. WHAT IS THE REQUIREMENT FOR CROSS-LAGGED MODELS IN MY CASE.Should i ONLy use the students who were involve in all the three time points for a cross-lagged model or i can use the data as it stands.
 Linda K. Muthen posted on Thursday, January 02, 2014 - 11:47 am
As long as you believe all subjects come from the same population, I would use all available information.
 emmanuel bofah posted on Saturday, January 04, 2014 - 6:45 am
why is it that all cross-lagged models have seen have TWO constructs measured at different time points. Is it a necessary condition or because of complexity.
 Linda K. Muthen posted on Saturday, January 04, 2014 - 1:10 pm
A cross-lagged model requires a minimum of two constricts. You can have more. General questions like this are more appropriate for a general discussion forum like SEMNET.
 hazel liao posted on Thursday, May 21, 2015 - 8:22 am
Hi~
I want to use cross-lagged panel to analysis my data.
There is tow point time longitudinal data.
One of the variable data is normal, so I used ML estimator to conduct CFA to determine the latent variable.
The other data is not normal distributed, so I used MLM estimator to conduct CFA to determine the latent variable.
However, I want to use these tow latent variables which are used different estimator in the CFA to conduct cross-lagged panel. The question is what estimator should I use when I use cross-lagged panel to analysis my data? ML? MLM?

ps. I had used the ML to conduct cross-lagged but the model fit are really poor. Then, I changed the estimator to MLM, the model fit are much better.
 Linda K. Muthen posted on Thursday, May 21, 2015 - 2:54 pm
I would recommend using MLR in both cases. It is also robust to non-normality.
 hazel liao posted on Friday, May 22, 2015 - 4:57 am
Thank you for quickly reply.

Here is one thing I want to make sure, you mean MLR estimator can also be used in the normality data?
 Linda K. Muthen posted on Friday, May 22, 2015 - 7:13 am
Yes.
 hazel liao posted on Friday, May 22, 2015 - 8:20 am
Thank you very much, I will try it.
 hazel liao posted on Wednesday, May 27, 2015 - 2:09 am
Hi~ I have one more question. Could the categorical data which has 4 categories be regarded as non-normality data? And, could I still use the MLR estimator to the categorical data?

Thank you~
 Linda K. Muthen posted on Wednesday, May 27, 2015 - 6:20 am
MLR is robust to non-normality of continuous variables. Categorical data methodology takes care of any floor or ceiling effects of categorical variables. Using WLSMV or MLR and the CATEGORICAL option takes care of this.
 hazel liao posted on Wednesday, May 27, 2015 - 8:16 am
Floor effects means almost item's response are 1?
Ceiling effect means almost item's response are 4?
 Linda K. Muthen posted on Wednesday, May 27, 2015 - 10:09 am
Yes.
 hazel liao posted on Wednesday, May 27, 2015 - 10:35 am
Thank you ~~~

1. You said MLR is robust to non-normality of continuous variables. However, when I use MLR and the CATEGORICAL option at the same time, in this way categorical data could be analyzed?

2. I want to use cross-lagged panel to analyze data, however a latent variable is from CFA using MLR estimator, the other latent variable is from CFA using WLSMV estimator. In this case, what estimator should I use to conducting cross-lagged?
 Bengt O. Muthen posted on Wednesday, May 27, 2015 - 1:30 pm
1. I am not sure what you are asking, but when declaring your variables as Categorical the non-normality robustness is not relevant. And you don't want to replace the Categorical statement with asking for MLR. Asking for Categorical and MLR is fine.

2. Either is fine.
 hazel liao posted on Saturday, May 30, 2015 - 12:03 am
Thank you for your response!

I have try the WLSMV estimator to analyze the categorical data, but why there is no residual variance in the model result?

And where could I find the reference to interpretation of the Threshold?

Thanks!
 Bengt O. Muthen posted on Saturday, May 30, 2015 - 8:20 am
Study up on categorical factor analysis in our short course for Topic 2 - see the handout and video on our website. This gives you all the answers.
 hazel liao posted on Saturday, May 30, 2015 - 8:27 am
Thanks for your information ^^
 Kelly M Allred posted on Tuesday, October 11, 2016 - 11:45 am
I am running the following cross-lagged model and in the output Mplus is automatically providing covariances between the outcomes variables (Pos_T2, Neg_T2, and Dest_T2). Is there any way to prevent Mplus from automatically specifying these covariances?

Pos_T2 on Pos_T1 Neg_T1 Dest_T1;
Neg_T2 on Pos_T1 Neg_T1 Dest_T1;
Dest_T2 on Pos_T1 Neg_T1 Dest_T1;

Pos_T1 with Neg_T1 Dest_T1;
Neg_T1 with Dest_T1;

Dep_T1 with Pos_T1 Neg_T1 Dest_T1;
 Bengt O. Muthen posted on Tuesday, October 11, 2016 - 12:11 pm
Yes, you can say e.g.

Pos_T2 WITH Neg_T2@0;

But they are typically significant.
 Dana Vertsberger posted on Sunday, March 25, 2018 - 8:49 am
Hi,

I need to run two identical but separate cross-legged models - one for mothers and one for fathers from the same family and I want to see if their paths are significantly different from one another.

Ideally, I would use the grouping option, and then fix the parameters of interest to be equal across groups, then compare a model with the parameters constrained and then with them free using an equal fit test.

However, the two groups are dependent - mothers and fathers from the same family.

Is there any way to take this dependency into account?

Thank you very much,
Dana
 Bengt O. Muthen posted on Sunday, March 25, 2018 - 12:00 pm
You can put mothers and fathers in the same model to account for them coming from the same family. So if you have T times points and 2 repeated measures outcomes, you will have a data set with 2*T + 2*T columns (mother + father). So end up with 4 processes, one for mother and one for father.

You can then decide how the 2 processes should be correlated. And you can easily test equalities.

Also, check out our new FAQ:

RI-CLPM Hamaker example
 Dana Vertsberger posted on Monday, March 26, 2018 - 5:53 am
Hi,
Thank you for your reply.
I think I didn't explain myself - I have a mother-child model, and a father-child model, and I want to see whether the cross-lagged paths in the mother-child model are significantly different from the paths in the father-child model.

How can this be achieved?

Thank you very much
Dana
 Bengt O. Muthen posted on Monday, March 26, 2018 - 10:22 am
I don't know the design. Is the child outcomes the same (same variables, same values) in the mother-child model as in the father-child model so that there are only 3 processes?
 Dana Vertsberger posted on Monday, March 26, 2018 - 10:44 am
Hi,

For each parent I have 2 variables (the same variables) and one child variable.
I want to see whether there are bidirectional associations over time between each parent and the child, and to see whether these associations differ between the parents.

Can this be done in a cross-lagged model?
 Bengt O. Muthen posted on Monday, March 26, 2018 - 11:01 am
Unless the child variable has the same values for the mother and father model, that sounds like you observe 4 different (different in values) processes over time. But where this is structured as 2 model parts, each with 2 processes. This can be done in an extended cross-lagged model. If I now understand you correctly, we can discuss how.
 Dana Vertsberger posted on Tuesday, March 27, 2018 - 12:45 am
Hi,
The child variable does have the same values for fathers and mothers model - they are observations of the child and are separate from the parents variables which are parents self-reported questionnaires.
 Bengt O. Muthen posted on Tuesday, March 27, 2018 - 10:32 am
Ok, so then you have 3 processes: mother, child, father. So you have 3*T columns in your data. You can think of how to specify it in Mplus by first drawing a model diagram where in the top row, from left to right, you have the mother variable at the different time points, below that you have the row of child variables, and below that a row of the father variables. So 3 rows of variables. You have a cross-lagged model for row 1 and 2, and another cross-lagged model for row 2 and row 3. If you go by the RI-CLPM, you have 3 random intercept factors which are all correlated. This means that the dependence between mother and father variables is accounted for. This also makes it easy to test for equalities across mother-father.
 Simon Coulombe posted on Monday, September 24, 2018 - 6:13 pm
Hi,
I conducted a cross-lagged path analysis in Mplus

1. A reviewer is saying that, when testing a model without latent variables (path analysis), fit indices are about useless. What do you think? Is it supported by research?

2. Also, we wanted to control for the effects of demographics variables. We included pathways between the demographics and T1 variables, but the reviewers says we should have included the links from demographics to T2 variables instead. What do you think?

Thank you

Simon
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