I am struggling with my moderation model. All my variables are at the within-level and continuous. I would like to test if women's shame (ShameW) moderates the associations between women's empathy (EmpW) and partners' empathy (EmpP) on women's pain (Pain) and both partners' distress (DetW and DetP) :
USEVARIABLES = EmpW EmpP Pain DetW DetP ShameW IntW IntP; CLUSTER = Couple ; WITHIN EmpW EmpW ShameW IntW IntP Pain DetW DetP ; DEFINE: IntW=EmpW*ShameW ; IntP=EmpP*ShameW ; CENTER EmpW EmpP ShameW Pain DetP DetP (GROUPMEAN); ANALYSIS: TYPE = twolevel random ; MODEL: %BETWEEN% %WITHIN% Pain ON EmpW ShameW EmpP IntW IntP; DetW ON EmpW ShameW EmpP IntW IntP; DetP ON EmpP ShameW EmpA IntW IntP; Pain WITH DetW DetP ; DetP WITH DetW ; EmpW WITH EmpP ; MODEL CONSTRAINT: NEW (LOW_M MED_M HIGH_M); LOW_M = 0.85; ! - SD MED_M = 3.37; ! mean value HIGH_M = 5.89; ! + SD
I am stuck here. I probably have to calculate simple slopes for each value of the moderator and use loop plot to plot model for low, med, high values of the moderator, but I don't know how to write the syntax so I could have the interaction effects.
If this syntax is not the best, do you have another one?
I looked at the script in Table 1.8, part 2, p. 31. I fear that what I am struggling with is the formula, which directly translate into my syntax.
I understand with a dyad dataset (no moderator), we would have two equations in SEM: Y1 = b0 + b1X1 + b2X1 + e1 Y2 = b0 + b2X1 + b1X1 + e2 and with a moderator we would have (correct me if I am wrong) : Y1 = b0 + b1X1 + b2X2 + b3M + b4X1M + b5X2M + e1 Y2 = b6 + b7X2 + b8X1 + b9M + b10X2M + b11X1M + e2
In your example you wrote : model constraint: LOOP(x,-1,1,0.1); PLOT(effect); effect = b1 + b3*x;
However, could you help me with the 'effect formula' part? Should I have two lines here (dyad dataset)?
Lastly, you set 0.1 increment in your example. Is it the usual choice?
No, you should not add the 2 terms (b1 + b4*LOW_M)+(b2 + b5*LOW_M); they represent different things - the first part is the X1 effect and the second part is the X2 effect. You can look at each separately.
I think you should consult with a statistician - someone who is familiar with moderation modeling. We can not teach it here.