Hello SAS Community,
I am having some difficulty interpreting my results using Casual Mediation.
My mediator variable (wealthy) is binary (0 or 1). 0 being not wealthy and 1 being wealthy
My treatment variable (race) is binary as (0 or 1). 0 being white and 1 being minority
My outcome variable is test scores. This variable is continuous.
This is code I used
proc causalmed data=scores;
class race wealthy;
model scores = race wealthy race*wealthy;
mediator wealthy = race;
bootstrap;
run;
These are my results and there is significance, but I am having trouble with the interpretation as I do not work with binary variables often.
Summary of Effects |
|||||||||
|
Estimate |
Standard |
Bootstrap |
Wald 95% |
Bootstrap Bias Corrected |
Z |
Pr > |Z| |
||
Total Effect |
-7.2053 |
1.8438 |
1.7369 |
-10.8192 |
-3.5915 |
-10.6610 |
-3.7261 |
-3.91 |
<.0001 |
Controlled Direct Effect (CDE) |
-15.2432 |
3.6224 |
3.9705 |
-22.3430 |
-8.1435 |
-22.8759 |
-7.1777 |
-4.21 |
<.0001 |
Natural Direct Effect (NDE) |
-4.7966 |
1.9995 |
1.8347 |
-8.7156 |
-0.8777 |
-8.4364 |
-1.2785 |
-2.40 |
<.0001 |
Natural Indirect Effect (NIE) |
-2.4087 |
0.9230 |
1.0044 |
-4.2178 |
-0.5995 |
-4.9271 |
-0.8741 |
-2.61 |
<.0001 |
Percentage Mediated |
33.4290 |
14.8643 |
16.0116 |
4.2955 |
62.5624 |
11.1562 |
76.7381 |
2.25 |
<.0001 |
Percentage Due to Interaction |
-93.0785 |
41.8649 |
48.0093 |
-175.13 |
-11.0247 |
-218.73 |
-31.5048 |
-2.22 |
<.0001 |
Percentage Eliminated |
-111.56 |
53.7929 |
61.2548 |
-216.99 |
-6.1238 |
-267.02 |
-23.3474 |
-2.07 |
<.0001 |
Thank you very much for your help!
Mark
You are asking what percent of the effect of race on test scores is mediated by differences in income. The answer from the table is 33% (Percentage Mediated) and this is statistically significant (p<0.0001). In any model, the factor is always on the right and the outcome is on the left. The mediator statement shows the mediator model (wealthy = race). Hence, the direct pathway is race -> test scores and the indirect (mediated) pathway is race-> wealth -> test scores. This can be drawn as a triangle with 33% over “->wealth->” and the remaining 66% over the direct “race -> test scores” arrow.
Yes, although we do not have directionality with causal mediation (but maybe there is - look at all the minus signs in the table!). It is tempting to say that part of the reason why individuals of minority status have lower test scores is because of lower income, but that would need further exploration. Right now, it would probably be more accurate to conclude that race has an effect on test scores partly due to differences in income.
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