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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. 


Hi, I need to run two identical but separate crosslegged 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 


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: RICLPM Hamaker example 


Hi, Thank you for your reply. I think I didn't explain myself  I have a motherchild model, and a fatherchild model, and I want to see whether the crosslagged paths in the motherchild model are significantly different from the paths in the fatherchild model. How can this be achieved? Thank you very much Dana 


I don't know the design. Is the child outcomes the same (same variables, same values) in the motherchild model as in the fatherchild model so that there are only 3 processes? 


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 crosslagged model? 


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 crosslagged model. If I now understand you correctly, we can discuss how. 


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 selfreported questionnaires. 


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 crosslagged model for row 1 and 2, and another crosslagged model for row 2 and row 3. If you go by the RICLPM, 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 motherfather. 


Hi, I conducted a crosslagged 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 


Hi, I'm working with a relatively small dataset of 296 individuals to look at crosslagged effects across two waves of data between two constructs. Given that my sample is large but not 'super' large, I decided to use a path analysis approach rather than a full structural equation model. The reviewers are commenting that I should have used a full model to account for measurement error. My worry related to using a full model is sample size. The full model would include about 220 free parameters, while the path analysis model would include less than 50 free parameters (including several control variables in both cases). I'm not sure what to do in that context? Thank you Simon 


This general analysis strategy question is suitable for SEMNET. 


I have a CLPM with 5 waves of data. With the model test command, i checked if the lagged effects differ over time (in order to simplify the model). MODEL TEST: 0=L1L2; 0=L1L3; 0=L1L4; The wald test was not significant, so I assumed that the lagged effects can be considered equal. Subsequently, I wanted to constrain these lagged effects, and I have two questions regarding this. First, I tried to do that using (1), e.g. i2 ON d1(1); i3 ON d2(1); i4 ON d3(1); i5 ON d4(1); The lagged effect in the constrained model is 0.114. The lagged effects in the unconstrained model are: lag 1: 0.235 lag 2: 0.050 lag 3: 0.106 lag 4: 0.157 Question 1: I was wondering where the value 0.114 is based on? Second, another option is to constrain the effects to the value of the first lag in the unconstrained model: i2 ON d1@0.235; i3 ON d2@0.235; i4 ON 3@0.235; i5 ON d4@0.235; Question 2: I am not sure what constraining method I should use. Can you give some advice about this? 


Q1: One can see it as some sort of weighted average of the unconstrained values where the weights are chosen so that the likelihood is maximized (so quite complex weights). Q2: You should not use option 2 because all you want to say is that they are equal (so using 1 parameter), not equal to a particular value (so using 0 parameters). 

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