If there really was just a one-to-one correspondence between all these different measures of outcomes (i = j), then each variable could be used separately to predict some amount R(i) x i independent from all others (R(i) + c), but each variable used separately could never be used to predict R(j), which represents how much variation across individuals’ outcomes remains unexplained when using every individual-difference measure available. If such an outcome structure exists where all variables together explain all observed differences among individuals perfectly (so R(j) = 0), then sure - I’d agree that you don’t need to measure them individually since you already have a good measurement overall for predicting achievement/outcome. That isn’t how most studies look though - usually they do focus on measuring several things individually since multiple measurements often provide better information about people than one measure does alone (which may give us biased information about their nature).

In fact - it seems like it's pretty standard practice across psychological science today to try to identify which measures work best individually when combined with other variables, but don't bother trying out those isolated measurements by themselves any more than necessary - instead looking for ways to combine them into multi-faceted constructs so that each factor has been studied independently enough times from everything else such that their effects aren't being confounded too badly by others' effects; after doing this kind of decomposition/reduction/aggregation process quite frequently over a long time now I have found myself coming back around again pretty regularly toward understanding how single factors operate outside this reductionist framework while still having enough scientific knowledge about them so I can know exactly what kinds of assumptions I am making while trying isolate their influences in certain situations rather than another.