“All of this, of course, is completely compatible with IQ having some ability, when plugged into a linear regression, to predict things like college grades or salaries or the odds of being arrested by age 30. (This predictive ability is vastly less than many people would lead you to believe [cf.], but I’m happy to give them that point for the sake of argument.) This would still be true if I introduced a broader mens sana in corpore sano score, which combined IQ tests, physical fitness tests, and (to really return to the classical roots of Western civilization) rated hot-or-not sexiness. Indeed, since all these things predict success in life (of one form or another), and are all more or less positively correlated, I would guess that MSICS scores would do an even better job than IQ scores. I could even attribute them all to a single factor, a (for arete), and start treating it as a real causal variable. By that point, however, I’d be doing something so obviously dumb that I’d be accused of unfair parody and arguing against caricatures and straw-men.”
This is the point here. There’s a difference between coming up with linear combinations and positing real, physiological causes.
My beef isn’t with Shalizi’s reasoning, which is correct. I disagree with his text connotationally. Calling something a “myth” because it isn’t a causal factor and you happen to study causal factors is misleading. Most people who use g don’t need it to be a genuine causal factor; a predictive factor is enough for most uses, as long as we can’t actually modify dendrite density in living humans or something like that.
If g is a causal factor then “A has higher g than B” adds additional information to the statement “A scored higher than B on such-and-such tests.” It might mean, for instance, that you could look in A’s brain and see different structure than in B’s brain; it might mean that we would expect A to be better at unrelated, previously untested skills.
If g is not a causal factor, then comments about g don’t add any new information; they just sort of summarize or restate. That difference is significant.
A predictive factor is enough for predictive uses, but not for a lot of policy uses, which rely on causality. From your comment, I assume you are not a lefty, and that you think we should be more confident than we are about using IQ to make decisions regarding race. I think that Shalizi’s reasoning is likely not irrelevant to making those decisions; it should probably make us more guarded in practice.
I don’t understand your last paragraph. Could you give an example? Is this relevant to the decision of whether intelligence tests should be used for choosing firemen? or is that a predictive use?
The kinds of implications I’m thinking about are that if IQ causes X, (and if IQ is heritable) then we should not seek to change X by social engineering means, because it won’t be possible. X could be the distribution of college admittees, firemen, criminals, etc.
Not all policy has to rely on causal factors, of course. And my thinking is a little blurry on these issues in general.
“All of this, of course, is completely compatible with IQ having some ability, when plugged into a linear regression, to predict things like college grades or salaries or the odds of being arrested by age 30. (This predictive ability is vastly less than many people would lead you to believe [cf.], but I’m happy to give them that point for the sake of argument.) This would still be true if I introduced a broader mens sana in corpore sano score, which combined IQ tests, physical fitness tests, and (to really return to the classical roots of Western civilization) rated hot-or-not sexiness. Indeed, since all these things predict success in life (of one form or another), and are all more or less positively correlated, I would guess that MSICS scores would do an even better job than IQ scores. I could even attribute them all to a single factor, a (for arete), and start treating it as a real causal variable. By that point, however, I’d be doing something so obviously dumb that I’d be accused of unfair parody and arguing against caricatures and straw-men.”
This is the point here. There’s a difference between coming up with linear combinations and positing real, physiological causes.
My beef isn’t with Shalizi’s reasoning, which is correct. I disagree with his text connotationally. Calling something a “myth” because it isn’t a causal factor and you happen to study causal factors is misleading. Most people who use g don’t need it to be a genuine causal factor; a predictive factor is enough for most uses, as long as we can’t actually modify dendrite density in living humans or something like that.
Ok, let’s talk connotations.
If g is a causal factor then “A has higher g than B” adds additional information to the statement “A scored higher than B on such-and-such tests.” It might mean, for instance, that you could look in A’s brain and see different structure than in B’s brain; it might mean that we would expect A to be better at unrelated, previously untested skills.
If g is not a causal factor, then comments about g don’t add any new information; they just sort of summarize or restate. That difference is significant.
A predictive factor is enough for predictive uses, but not for a lot of policy uses, which rely on causality. From your comment, I assume you are not a lefty, and that you think we should be more confident than we are about using IQ to make decisions regarding race. I think that Shalizi’s reasoning is likely not irrelevant to making those decisions; it should probably make us more guarded in practice.
I don’t understand your last paragraph. Could you give an example? Is this relevant to the decision of whether intelligence tests should be used for choosing firemen? or is that a predictive use?
The kinds of implications I’m thinking about are that if IQ causes X, (and if IQ is heritable) then we should not seek to change X by social engineering means, because it won’t be possible. X could be the distribution of college admittees, firemen, criminals, etc.
Not all policy has to rely on causal factors, of course. And my thinking is a little blurry on these issues in general.
Seconding Douglas_Knight’s question. I don’t understand why you say policy uses must rely on causal factors.