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
where c is agg1, which has been standardized.
However dir is given as
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
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 zero-inflated Poisson distribution?