I can't tell quite whether any of your variables are involved in grouping for your selection criteria or not. I am going to assume so as that is the slightly more complex version for selecting sample.
Your " random sample sizes of the original set (say, 25%, 50%, 75% of participants) 10000 times" can be accomplished with Proc Surveyselect and the Reps (replicates) to generate multiple samples using similar options.
Here is an example using the SASHELP.Class data set as an example. It is small enough that you can compare the result to the basic set relatively easily.
/* sort data so we can use Strat
based on sex later
*/
proc sort data=sashelp.class
out=work.class;
by sex;
run;
proc surveyselect data=work.class
out=work.sample
samprate=.25 reps=10;
strata sex;
run;
The Samprate in the example selects a 25% sample from each level of the Strata variable Sex. The Reps=10 says to create 10 samples. There will be a variable added to indicate which replicate a group of records comes from.
If you wanted to run a separate analysis for each level of Sex and 10 separate regressions you would use a
By Sex Replicate;
in the regression.
If you don't need strata, which could be several variables but the input needs to be sorted by all, then drop the strata statement.
I suspect that Race, Target and Distractor would be CLASS variables and used in your model to get comparisons between the levels (or combinations).
I would suggest, if you intend to combine any of these sets to make sure there is a variable indicating the source. The indsname option at the time you combine sets can make this easy. Example
data junk;
set sashelp.class
sashelp.cars (obs=20)
indsname=ds
;
source=ds;
run;
Not a particularly useful set but the object is to show there is a variable added to the data named Source and has the name of the data set each record came from.
