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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 CROSSLAGGED MODELS IN MY CASE.Should i ONLy use the students who were involve in all the three time points for a crosslagged model or i can use the data as it stands. 


As long as you believe all subjects come from the same population, I would use all available information. 


why is it that all crosslagged models have seen have TWO constructs measured at different time points. Is it a necessary condition or because of complexity. 


A crosslagged 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 crosslagged 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 crosslagged panel. The question is what estimator should I use when I use crosslagged panel to analysis my data? ML? MLM? ps. I had used the ML to conduct crosslagged but the model fit are really poor. Then, I changed the estimator to MLM, the model fit are much better. 


I would recommend using MLR in both cases. It is also robust to nonnormality. 

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? 


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 nonnormality data? And, could I still use the MLR estimator to the categorical data? Thank you~ 


MLR is robust to nonnormality 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? 


Yes. 

hazel liao posted on Wednesday, May 27, 2015  10:35 am



Thank you ~~~ 1. You said MLR is robust to nonnormality 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 crosslagged 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 crosslagged? 


1. I am not sure what you are asking, but when declaring your variables as Categorical the nonnormality 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! 


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 ^^ 


I am running the following crosslagged 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; 


Yes, you can say e.g. Pos_T2 WITH Neg_T2@0; But they are typically significant. 

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