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I'm trying to run a multilevel SEM model with a level 1 predictor, a level 1 mediator, and a level 1 moderator. Is there any sample code available for such a model? |
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I don't think so, but in line with single-level analysis you can define a level-1 interaction between the moderator and the covariate or mediator. Try it out. |
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Would a random effect on the moderator and/or the product term throw things off? |
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I'm combining Preacher, Rucker, & Hayes (2007; the A ->B path is moderated), w/ Preacher, Zyphur, & Zhang (2010): DEFINE: ASIDRS=ASI*DRS; MODEL: %WITHIN% MFQfreq ON ASI (a1w); MFQfreq ON DRS; MFQfreq ON ASIDRS (a3w); PSWQ ON MFQfreq (bw); PSWQ ON ASI; %BETWEEN% ASI MFQfreq PSWQ ASIDRS; MFQfreq ON ASI (a1b); MFQfreq ON DRS; MFQfreq ON ASIDRS (a3b); PSWQ ON MFQfreq (bb); PSWQ ON ASI; MODEL CONSTRAINT: NEW(indb indw wmodval); wmodval=-1; indw=(a1w + a3w*wmodval)*bw; indb=(a1b + a3b*wmodval)*bb; I get: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.176D-10. PROBLEM INVOLVING PARAMETER 10. THE MODEL ESTIMATION TERMINATED NORMALLY There are no cat. vars. Par. 10 is alpha for MFQFREQ. |
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Answer to the question in your first message: No. Answers to the second message: This may happen if any of your variables ASI, DRS are binary. You should also consider centering each of these variable before creating their interaction. Furthermore, I assume that you have more than 10 clusters and that you get SEs. |
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Thank you for your help -- there are no binary variables, and I have 50 clusters and can get SEs. I centered in SPSS and this worked perfectly. However, that means that the centering was grand mean rather than group mean. Is that appropriate? Or do I need to group mean center? |
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Sounds like there is another reason for the message; please send to Support. |
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This is a project a student is working on -- I'll have her send it to you shortly. In general, my understanding is that this sort of model should be group mean centered. Is that correct? |
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Why do you say it should be group-mean centered? |
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I guess I'm thinking about traditional multilevel modeling and the within subjects model for MSEM, where I think group mean centering is appropriate. I do see the issue with group mean centering and the between model, as it doesn't make sense there. |
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I am trying to specify a 1-1-1 model as suggested by Preacher, Zyphur, & Zhang (2010). I used STAND on the Output command but I don't receive stndardized results for "New/Additional Parameters". Are they not available? -Katrin |
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No, they are not available. You would need to specify the standardized parameter in MODEL CONSTRAINT to obtain a standard error. |
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Lewina Lee posted on Saturday, March 29, 2014 - 3:14 pm
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Dear Drs. Muthen, I am conducting a deconflated 1-1-1 multilevel mediation with an L2 moderator. Is my code correct? WITHIN ARE lneg cesd; BETWEEN ARE L18T lneg_m cesd_M L18_LNEG; !LNEG_M & CESD_M = group-specific means of LNEG & CESD; DEFINE: CENTER lneg cesd (GROUPMEAN); L18_LNEG = L18T*LNEG_M; ANALYSIS: TYPE = TWOLEVEL RANDOM; INTEGRATION = MONTECARLO; ALGORITHM=INTEGRATION; MITERATIONS = 700; MODEL: %WITHIN% sa| CESD on LNEG; sb| SIM on CESD; sc| SIM on LNEG; cesd lneg sim; [LNEG@0]; !grp-mean centered; [CESD@0]; %BETWEEN% [sa] (a); [sb] (b); [sc] (c); sb@0; sc@0 sa@0; SIM on CESD_M (bBTW); SIM on LNEG_M; CESD_M on LNEG_M (aBTW); CESD_M on L18T; CESD_M on L18_LNEG (gBTW); sa on L18T (gW); SIM; MODEL CONSTRAINT: new (indB0 indB2 indW0 indW2 mod0 mod2); mod0 = 0; mod2 = 2; indB0 = (aBTW+gBTW*0)*bBTW; indB2 = (aBTW+gBTW*2)*bBTW; indW0 = (a+gW*0)*b; indW2 = (a+gW*2)*b; Thanks, Lewina |
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Looks right. |
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Lewina Lee posted on Sunday, March 30, 2014 - 5:37 pm
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Thanks, Dr. Muthen. Can I ask a follow-up question: My dataset contains data from 3 occasions (Level 1) per person (Level 2). I'm treating the multilevel model as a repeated measures design without explicitly modeling how the dependent variable changes over time. Is there a limit to the number of random effects I can include? Lewina |
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Each random effect requires one dimension of integration. We don't recommend more than four. Otherwise, there is no limit. |
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Lewina Lee posted on Monday, March 31, 2014 - 9:45 am
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Thanks, Linda! |
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Jie Wang posted on Tuesday, February 03, 2015 - 12:02 am
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Dear Drs. Muthen, I want to test some conditional indirect effects of quadratic terms in a fully nested data (Level 1 variables only, no level 2 variables). I have already grand mean centered all IVs. I am really not sure about if I write correct syntax (especially the MODEL CONSTRAINT part). Can you have a look at my syntax and give me some comments? Thanks! бн DEFINE: x1sq = x1*x1; DEFINE: x2sq = x2*x2; DEFINE: inter11 = x1*mod1; DEFINE: Inter12 = x1*x1*mod1; DEFINE: Inter21 = x2*mod2; DEFINE: Inter22 = x2*x2*mod2; ANALYSIS: TYPE IS TWOLEVEL; MODEL: %WITHIN% mediator ON x1 x2 x1sq (a1w) x2sq (a3w) mod1 mod2 inter11 inter12 (a2w) inter21 inter22 (a4w); dv ON mediator (bw); %BETWEEN% mediator ON x1 x2 x1sq (a1b) x2sq (a3b) mod1 mod2 inter11 inter12 (a2b) inter21 inter22 (a4b); dv ON mediator (bb); MODEL CONSTRAINT: LOOP(mod,-2,2,0.1); NEW(ind1b ind1w ind2b ind2w); ind1w=(a1w+a2w*mod)*bw; ind1b=(a1b+a2b*mod)*bb; ind2w=(a3w+a4w*mod)*bw; ind2b=(a3b+a4b*mod)*bb; OUTPUT: TECH1 TECH8 CINTERVAL; |
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You use the a1w slope for the squared x1 term, but leave out the unsquared x1 term; when x1 changes, both terms have effects. You may want to ask this general modeling question on SEMNET. |
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