Same concept as @txnelson but I would arrange the table just a little differently by stacking to make a very long table, then splitting by the group column. Finally use the parameter in the by column of a bivariate script:
names default to here(1);
Long = Data Table( "DEBUG" ) << Stack(
columns( :paramA, :paramB, :paramC ),
Source Label Column( "Parameter" ),
Stacked Data Column( "Data" )
);
repivot = Long << Split(
Split By( :GROUP ),
Split( :Data ),
Group( :Parameter, :ID ),
Sort by Column Property
);
biv = Bivariate( Y( :A ), X( :B ), Summary Statistics( 1 ), By( :Parameter ) );
dtSum = (biv << XPath("//TableBox"))[1][1] << make combined data table;
dtSum << Select Where(:Column 1 != "Correlation");
dtSum << Delete Rows;
dtSum << Set Name( "Results" );
Then the resulting table can be sorted to find the interesting correlations.
The response screening platform certainly seems promising though.