This topic came up in the thread shown below, and it was made clear that Saul Reiser blew his analysis, as those omitted variables were very clearly in the report (further posts should be directed to that thread):
You’ve linked this twice as suggesting the original study was flawed, so I wanted to respond to it.
As a former quant, I used regression all the time. Linear regression is a very useful tool, as useful as a drill for someone doing woodworking. One of its advantages is that once a person understands the limits of the method, it is very easy to use, computationally fast, and quite often provides useful information.
But just as you need more than a drill to do woodworking, you need more than linear regression to perform analysis. In woodworking sometimes you need a drill, but other times you need a hammer, saw, chisel, screwdriver, clamp, etc.
Saul Geiser’s response talks about omitted variable bias. He runs a regression that suggests that adding student demographics makes HSGPA a more powerful predictor than SAT/ACT scores. Note that he doesn’t say that the effects of the SAT go to zero, just that the HSGPA becomes a stronger predictor than SAT/ACT scores. In fact, after adding high …