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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 ^^
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