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Anonymous posted on Tuesday, January 08, 2013  7:20 am



I have set up the following mediation model in Mplus using WLSMV, where Y is a dichotomous outcome variable, M is a continuous mediating variable, and age and age2 are centered continuous predictors: Y on age (p1); Y on age2(p2); Y on M (p3); M on age (p4); M on age2 (p5); [Y] (i1); [M] (i2); I can estimate the direct probability of Y for any given age using the model constraint command with phi(i1(p1*age)(p2*age2)). Can I estimate the indirect probability of Y for any given age similarly? I would like to construct a plot of the proportion indirect effect/total effect as a function of age, or something analogous. Thank you 


See the recent discussion thread with Todd Hartman starting January 5. 

Anonymous posted on Thursday, January 31, 2013  7:13 am



This is an extremely helpful paper! I have one question regarding the case of a binary outcome and continuous mediator: In the Mplus code, Table 32, Page 118, I was expecting the dir line to be something of the form dir=beta3*gamma0+beta2+beta3*gamma2*c where c is agg1, which has been standardized. However dir is given as dir=beta3*gamma0+beta2. Could you explain why this is please? Is this because the direct effects would need to be estimated for different specified values of c=agg1, and hence everything has been calculated conditional for the mean value for agg? 


Your dir formula is correct; that's the general form. In Table 32 the direct effect is evaluated at the average of c (agg1) which is zero in this case so the last term falls out. 

Anonymous posted on Monday, February 04, 2013  4:46 am



Thank you. I have now fitted my model: there are two exogenous predictors X and Xsquared (continuous), a continuous mediating variable M and a dichomotous outcome Y. The effect associated with M is allowed to vary with X (p<0.001). Using the results from the paper I am now able to estimate parameters for: direct effect associated with X on Y (a) direct effect associated with Xsq on Y (b) indirect effect of X on Y (c) indirect effect of Xsq on Y (d). I'd like to convert these results onto a probability scale as they are not very interpretable as they stand. In the paper you show how this can be done when the exogenous variable is binary (treatment/no treatment) and there is only one term. I have 2 continuous terms. Can this be extended easily to the above example? Is it correct to estimate for each value of X the total probability of Y as phi([Y$1]((a+c)*x))((b+d)*xsquared))) and the direct probability as phi([Y$1](a*x)(c*xsquared))? 


Page 16 of my paper shows how to express the effects on a binary outcome in the probability scale. When X is a continuous instead of a binary variable, the formulas are modified in line with VanderWeele and Vansteelandt (2009, Appendix). The direct and indirect effects are DE=(\beta_2+\beta_3 \gamma_0)) \; (xx'), TIE = (\beta_1 \gamma_1+\beta_3 \gamma_1 x) (xx'). For example, x' may represent the mean of X and x may represent one standard deviation above the mean. If X is standardized this results in the same formulas as for a 0/1 X variable. If X is centered, x'=0 and x is the standard deviation of X. 

Anonymous posted on Wednesday, February 13, 2013  8:38 am



Is it possible in Mplus to have a count variable as a mediator which is given a zeroinflated Poisson distribution? 


I don't know how that would be done. In the m>y relationship it isn't clear how m should be treated. The indirect effect is also unclear. 

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