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Is it possible to test the moderating effect of M (M is a moderators which is subject to influence of N) on the path from X (independent variable) to Y (dependent variable). I think this will be a multilevel moderation on path from X to Y. (X>Y is moderated by N>M). Can you please guide me through the process? 


I don't understand your model. What role exactly does N play? What does "X>Y is moderated by N>M" mean? Why do you say it is multilevel? Please write out the full model in some way. 


HI, I am sorry I did not explain it well. You can see the visual representation of my model following the link http://postimg.org/image/g0ex0qvtr/. The model has 3 IVs (lets say A, B and C) and 1 DV (Z). The paths between IVs and DV is moderated my M and P. Moderator M is further influenced by N (e.g. M can represent 'trust' and N is 'trust determinant'). 


With a continuous moderator such as your M, you create interaction variables (products) in Define. So for instance am = a*m; Apart from that you simply say Z on ac am .... ; M on n; 


I used the DEFINE command but I have an error message 'undefined variable used in transformation' 


Please send the output and your license number to support@statmodel.com. 

Dan R. posted on Thursday, May 28, 2015  6:01 pm



For the time being, I've dropped the 111 mediation component from my model to first look at the level 2 moderation effect. My question is whether it looks like I set up my level 2 moderation effects (Y ON XW & Y ON MZ) appropriately (i.e., if these regressions are significant, can I claim level two moderation is supported). Here is the model with simplified variable names: Usevariables are Y X M W Z XW MZ; WITHIN = X M; BETWEEN = W Z; CLUSTER = Subject; DEFINE: XW=X*W; MZ=M*Z; Analysis: TYPE = TWOLEVEL; ESTIMATOR=MLR; Model: %WITHIN% Y ON X M; Y M; %BETWEEN% Y W Z XW MZ; Y ON W; Y ON XW; Y ON Z; Y ON MZ; Output: sampstat stdyx Tech1 Tech8; 


Looks ok so far. 

Dan R. posted on Friday, May 29, 2015  2:39 am



Thank you, Bengt. And for mediation tests at level 1 and level 2, have I set the following up correctly? Given the interaction effects, my understanding is that the overall chisquare and traditional fit statistics aren't available. So, should I report the BIC and SRMR for comparisons? I actually do get a Chisquare x(12)=6.213, but I'm not sure if this is for the overall multilevel model or just a portion of the model. Any insights you could provide would be greatly appreciated. Usevariables are Y X M W Z XW MZ; WITHIN = X M; BETWEEN = W Z; CLUSTER = Subject; DEFINE: XW=X*W; MZ=M*Z; Analysis: TYPE = TWOLEVEL; ESTIMATOR=MLR; Model: %WITHIN% Y ON X (cw); Y ON M (bw); M ON X (aw); Y M; %BETWEEN% Y W Z XW MZ; Y ON W; Y ON XW (cb); Y ON Z; Y ON MZ (bb); MZ ON XW (ab); MODEL CONSTRAINT: New(indirw directw totalw indirb directb totalb); indirw = aw*bw; directw = cw; totalw = cw+aw*bw; indirb = ab*bb; directb = cb; totalb = cb+ab*bb; Output: sampstat stdyx Tech1 Tech8; 


Your Between model looks strange. The "MZ" variable name sounds like an interaction term and it is not declared as Within or Between. 

Dan R. posted on Friday, May 29, 2015  6:21 pm



The MZ variable is an interaction  M is measured at level 1, and Z is measured at level 2. If I try to specify MZ on the between = line, I run into the following error: *** ERROR One or more betweenlevel variables have variation within a cluster for one or more clusters. Check your data and format statement. Between Cluster ID with variation in this variable Variable (only one cluster ID will be listed) AMG_NEGA 531 DLP_EF 531 Any thoughts? 


You typically don't have an interaction variable as a dependent variable as you do on Between saying MZ ON XW. MZ is not a betweenlevel variable given that M is measured on the within level. I don't see which kind of interaction model you are after for Between, that is, it isn't clear if you want to moderate the x>m, x>y, or m>y paths. Putting aside that you are doing this on Between for 2level model, maybe you want to study the Preacher et al (2007) MBR article on moderated mediation (singlelevel) to see how such interaction models are set up. 

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