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Hopefully not a stupid question... However. If I conduct a model with a random intercept I am given the regression estimates with the withinlevel estimates as well as the between. However, if I use a random slopes model, the within regression equation is used to define the slope. In other words what was: y ON x1 x2 x3 Become s  y ON x1 x2 x3 Consequently the regression coefficients from Y on X1, X2 and X3 disappear from the output and I am left only with the estimate of S. Is there a way to include the coefficients of Y on X1, X2 and X3 in the output while still allowing the slope of Y to vary randomly? 


This statement is not Mplus syntax: s  y ON x1 x2 x3 A random slope s refers to a single coefficient so you should have just one x on the righthand side. If you want 3 of them, you should specify one at a time: s1, s2, s3. 


Hi Dr Muthen, I am Ahmad. Firstly, I would like to say Thank you to you as your software, user guide and online interaction/discussions helped me a lot throughout my degree. I read a lot of literature talking about 211 model or 121 model. However,my model is 112 (leader behaviours measured at individual level, team processes measured at individual level, and team performance measured at team level). I am new to using Mplus and unable to identify the right syntax to test the direct and mediating effects in my model. May I please ask for your help? Thank you so much once again. 


USEVARIABLES are Performance Leadership Processes; BETWEEN= Performance; WITHIN= Leadership Processes; Cluster is TeamID; Analysis: Type= Twolevel random; Model: %Within% Slope Processes on Leadership; %BETWEEN% Performance on Slope; This is what I understood uptil now to estimate cross level effect in my context of 112 model. 


You don't need a random slope. You can try this input using abbreviations of your variable names: Between = perf; %Within% beh with proc; %Between% perf on proc (b) beh (c); proc on beh (a); And then use Model Constraint with New(ind); ind = a*b; 


Thank you, Sir. I ran the model with this syntax and it worked. Few follow up questions please, Question 1: Is this the way to estimate 112 model (just to be absolutely sure)? Question 2: How can I infer that its a partial or full mediation? Question 3: How can I get the standardized estimates? Question 4: Is there any specific estimator that you would recommend me to use? MLR, MLF, or Bayes? Questions 5: The intercept value of outcome variable is negative and significant (not the process to performance relationship), does it matter? 


Q1: Yes. It is the betweenlevel part of beh and proc that play a role on the between level. Q2: Significance of "c". Q3: Ask for it in the Output command. For "a*b" you have to standardize yourself by dividing by the perf SD and multiplying by the beh betweenlevel SD. Q4: Bayes would be good. ML with bootstrap is in principle as good but Mplus doesn't have bootstrap for twolevel. Q5: The intercept is not the mean so I don't see how that matters. 


That's very helpful. Thankyou so much. If I have to test the individual level effect of behav (independent) on individual level proc (mediator) and team level perfor (outcome); meaning the cross level effect from individual level proc to team level perf. Would I be using the same analysis type and code? Thanks so much once again,Sir. 


That sounds like Between = perfor; %Within% proc on behav; %Between% perfor on proc; or with a random slope: %Within% s proc on behav; %Between% perfor on proc s; 


Thanks. I was after the second method which you showed. Also, in the second method, how will the indirect effect be calculated? 


The way I wrote it, there is no indirect effect but perhaps what you have in mind is adding one more line on Between defining a: %Within% s proc on behav; %Between% perfor on proc (b) beh (c); proc on beh (a); I just want to make sure that this is really what you have in mind. To handle the latent variable decomposition of behav in this random slope situation, you would then have to use Estimator=Bayes. See our new paper on our website: Asparouhov, T. & Muthén, B. (2018). Latent variable centering of predictors and mediators in multilevel and timeseries models. Technical Report, Version 2. August 5, 2018. Accepted for publication in Structural Equation Modeling. (Download scripts). 


Yes, Sir. This is exactly what I was asking. The mediating role in cross level effect using product of coefficient of both paths, behav (level 1) to process (level 1) to peformance (level 2). 


In this case the indirect effect is simply a*b. The mean of the slope s does not play a role in this case because your Y ("perfor") is on only the between level. 


Thank you. I have sent my license number, data and output file at support for your perusal. Just to completly sure about the syntax. 

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