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Dear Linda and Bengt, I am trying to run a moderated mediation model with a dichotomous moderator (w). For this I am using the following code: DEFINE: XW = x*w; ANALYSIS: TYPE = GENERAL; ESTIMATOR = ML; BOOTSTRAP = 10000; MODEL: [x]; [y] (b0); y ON m (b1); y ON x (cdash); [m] (a0); m ON x (a1); m ON w (a2); m ON XW (a3); MODEL CONSTRAINT: NEW(LOW_W HIGH_W IND_LOWW IND_HIW TOT_LOWW TOT_HIW); LOW_W = 0; HIGH_W = 1; IND_LOWW = a1*b1 + a3*b1*LOW_W; IND_HIW = a1*b1 + a3*b1*HIGH_W; TOT_LOWW = IND_LOWW + cdash; TOT_HIW = IND_HIW + cdash; Model is running using this, but I have to questions: 1. I have missing data on the x variable which I'd like to impute. Is there any option to do so? In the simple mediation model I fixed this by adding [x]; to the model command. But here it is not working doing so. 2. I would like to add a second x variable into the mediation model, while the moderator is only influencing the path x1m not the path x2m. Is this possible? Thanks a lot! 


1. I think you have to also mention the mean of the XW variable. 2. Yes, this is straightforward. 


1. It's running, thanks! 2. Am I doing it this way: MODEL: [y] (b0); y ON m (b1); y ON x1 (c1dash); y ON x2 (c2dash); [m] (a0); m ON x1 (a1); m ON x2 (a4); m ON w (a2); m ON XW (a3); MODEL CONSTRAINT: NEW(LOW_W HIGH_W IND_LOWW IND_HIW IND_x2 TOT_LOWW TOT_HIW TOT_x2); LOW_W = 0; HIGH_W = 1; IND_LOWW = a1*b1 + a3*b1*LOW_W; IND_HIW = a1*b1 + a3*b1*HIGH_W; IND_x2 = a4*b1; TOT_LOWW = IND_LOWW + c1dash; TOT_HIW = IND_HIW + c1dash; TOT_x2 = IND_x2 + c2dash; It's running but I am not sure if I get the indirect effects right? 


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Mark Burton posted on Friday, September 09, 2016  12:06 pm



Could you explain why adding the mean of a variable to the model syntax allows for analyses of the entire sample? How is it imputing missing data? For example, pertaining to the previous post, why does adding [X] to the model command "fix" this problem. I am running similar analyses and want to make sure I am not simply doing a mean replacement or something. Thank you! 


Mentioning the mean or variance of an x variable brings it into the model, that is, it is no longer conditioned on but treated like the y variables. The theory for this is explained in our new book (see our homepage). No data imputation is done but simply ML estimation under MAR  which sometimes called FIML. 

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