You not only need a test that predicts it initially but you would also need to have a test that’s resistent to optimizing for doing well on the test. As far as I understand we lack a nongameable test for conscientiousness that can be done in a short timeframe.
Big companies like Google test the effectiveness of their hiring criteria predicting employee success.
If you can make a good case that you have developed a superior psychometric tool for evaluating candidates to hire, there are big employers who would want to buy your tool. While a startup might not have to funds to invest into great psychometric tools big employers can and there’s a lot of money to be made by getting better at hiring and optimizing it.
While Google might be a forerunner at quantitatively evaluating their hiring criteria it should provide enough benefit to companies that over time all the large companies will do that.
When providing new ways of credentialing I consider it ineffective to ask: “How can we do what the status quo does?” In the status quo companies still make many bad hiring decisions.
A better question is “How can we better predict people’s performance?”
I think that Tetlock provided an answer with his work on experts in politics where he found predition making as a way to evaluate performance. I described how the same principle can work in medicine in Prediction-based Medicine.
You not only need a test that predicts it initially but you would also need to have a test that’s resistent to optimizing for doing well on the test. As far as I understand we lack a nongameable test for conscientiousness that can be done in a short timeframe.
Big companies like Google test the effectiveness of their hiring criteria predicting employee success.
If you can make a good case that you have developed a superior psychometric tool for evaluating candidates to hire, there are big employers who would want to buy your tool. While a startup might not have to funds to invest into great psychometric tools big employers can and there’s a lot of money to be made by getting better at hiring and optimizing it.
While Google might be a forerunner at quantitatively evaluating their hiring criteria it should provide enough benefit to companies that over time all the large companies will do that.
When providing new ways of credentialing I consider it ineffective to ask: “How can we do what the status quo does?” In the status quo companies still make many bad hiring decisions.
A better question is “How can we better predict people’s performance?”
I think that Tetlock provided an answer with his work on experts in politics where he found predition making as a way to evaluate performance. I described how the same principle can work in medicine in Prediction-based Medicine.