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Hi, I have two questions regarding the centering approach used in DSEM for the lagged variable and the level1 covariates respectively. (1) From my understanding, the latent centering approach is used for the variable with the autoregressive parameter by default. So in the following syntax, we use the raw (uncentered) data for y. Is this correct (in Mplus V8.0)? (2) Is latent centering approach also available for level1 covariate x, which has variability on both withinperson and betweenperson levels? If so, how do we specify it? Or do we have to use the groupmean center approach for x as follow? variable: lagged=y(1); within=x; define: center x (groupmean); model: %within% s1  y on y&1; s2  y on x; 


The Y variable is latent centered (since 8.0). The covariate X can be latent centered (since 8.1) using script like this variable: lagged=y(1); model: %within% s1  y on y&1; s2  y on x; %between% y; x; so you basically have to take out the observed group mean centering statement and the within= specification. No other changes are needed. 


Thank you very much for your explanation! I'm using Mplus 8.0 for DSEM analysis. Does it mean that in 8.0, the observed groupmean centering approach needs to be used for covariate X to get accurate estimates for withinperson and betweenperson effects? Thank you! 


You can use the observed centering approach if the cluster sizes is more than 100, but generally you should try to move to 8.2, not just because of the latent centering but also because you might want to use the RDSEM method instead and include autocorrelation for the covariate as well. See the first three papers http://statmodel.com/TimeSeries.shtml 


Hi, I am currently familiarizing myself with DSEM and have a question on the meaning of the intercept when using the latent covariate approach to centering. The model is simple: the dependent variable y (occasions nested in persons) is regressed on the time varying predictor variable x. The Mplus code is VARIABLE: NAMES = id y x; USEVARIABLES = y x ; WITHIN = ; BETWEEN = ; CLUSTER = id; ANALYSIS: TYPE = TWOLEVEL RANDOM; ESTIMATOR = BAYES; PROCESSORS = 2; BITERATIONS = (2000); MODEL: %WITHIN% s  y on x; %BETWEEN% y on x; x; [x]; y; [y]; s; [s]; y with s; The estimated intercept for y is very different from the intercept of a similar model in which I use the observed group mean centering approach as described in Asparouhov & Muthen (2018). Instead, the intercept seems to be reflect a value for x = 0. The estimated slope, instead, is quite similar to the slope of the group mean centering approach. That is, the latent mean centering approach seems to center the Level1 predictor x around the latent means of x (similar to what happens in group mean centering), but the Level2 part of x does not seem to be centered. Is my observation correct or am I doing something wrong here / misunderstand something? What exactly is happening here? Happy to hearing from you. Thank you. 


Correct. The Level2 part of x is not centered. Centering on Level2 is not as important as centering on Level1. Centering on Level2 is nothing more than subtracting a constant from a variable, i.e., it is a simple model reparameterization / change of scale. You can add DEFINE: CENTER x (GRANDMEAN); if you want it centered. 


Thank you! 

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