The predictor of my model is parents' educational levels. I have been using Hayes and Preacher (2014) suggestion to derive indirect, direct and total effects for each of the 3 dummy variables created from the 4-category predictor. All the effects are interpreted relative to the reference category (pared_1). y IND pared_2; y IND pared_3; y IND pared_4;
However, I am not completely sure if in causal mediation the TNIEs and PNDEs derived from every dummy variable of parents' education can also be interpreted relative to the reference category of parents' education.
y IND gpa pared_2; y IND gpa pared_3; y IND gpa pared_4;
y is binary and the mediator (gpa) is continuous
Or the TNIEs and PNDEs are actually comparing the dummy variable analysed against all other categories from the whole sample. For instance, pared_2=1 vs pared_2=0 which actually will mean comparing against all other individuals in the sample that have parents with other levels of education rather than 'pared_2' and not only the reference category?
My model is y ON gpa pared_2 pared_3 pared_4; gpa ON pared_2 pared_3 pared_4;
I found that 'medflex' (an R-package) explicitly takes into account multicategorical exposure variables in a causal mediation setting. Comparing indirect and direct effects derived from the same model above using medflex (without covariates or interaction between in the exposure and the mediator) gives me almost identical results are those derived in Mplus as z-scores (i.e. indir1= prob11_1-prob10_1, etc.).
I was thinking that I am finding the same results because I am actually comparing the dummy variable of interest including the constant/intercept which accounts for the effect of the reference category and setting aside the effect of the coefficients from the other dummies (not included in 'arg' variables). Therefore, Hayes and Preacher (2014) interpretation in the standard mediation modelling is also applicable in causal mediation in Mplus.
I was wondering whether in causal mediation having covariates as dummy variables might have an effect on the derivation/interpretation of causally-defined effects. For instance, adding the dummy variables of male and smoker as covariates to a mediation model will make that the constant and intercept terms now will be accounting for the effects of female and non-smoker alongside to the control group.
In conventional models (linear models for continuous DVs, no X, M interaction) the control variables (covariates) do not play a role in the indirect or direct effects. But in other cases they do and would be interpreted as you say.