Do you similarly control the IQ finding for height? For obvious reasons that is necessary for consistency.
If you do a linear regression with both IQ and height as inputs, this automatically separates their (linear) effects.
But I’m not sure about the general point, because IQ is an estimate of a factor and height is an estimate of a single variable. My impression is that when you are looking for the individual effect of a component of a hidden factor, then you want to control for the factor before measuring the effect of the component, but when measuring the effect of the hidden factor you don’t control for the components. But height isn’t a component of the IQ calculation, though it does appear to be weakly related to intelligence.
I’d certainly expect (based on loose memories of studies encountered) height to predict more than ‘0 to 0.1’ and IQ to predict a heck of a lot less than 0.6, especially when considering populations that are not limited to first world nations.
I’m also having trouble remembering if the 0.05 number I remembered was an r or r^2, which would significantly impact the range. I’m finding rs of about .2 for the correlation between height and intelligence, and rs of about .2 for the correlation between height and income without controlling for intelligence. Haven’t found anything yet that does control for it. (IQ correlation with income, as mentioned in a cousin comment, is about .4.)
In any case, I would consider it legitimate to readers who encounter “Life is an IQ test” delivered in response to the quoted context to be sufficient evidence that the comment is not worth reading and downvoting and ignoring it.
It’s still not clear to me what about the saying you find objectionable. The best guess I have is that you took it to mean that the g-loading of life success was comparable to Raven’s, when I meant that they were g-loaded at all. The heart of that paragraph:
To the extent that a medical condition makes someone test poorly, it generally also makes them live poorly.
seems to me like a fair explanation of the saying, and why it’s confused to think about nurture factors influencing intelligence as separate from actual intelligence.
If you do a linear regression with both IQ and height as inputs, this automatically separates their (linear) effects.
But I’m not sure about the general point, because IQ is an estimate of a factor and height is an estimate of a single variable. My impression is that when you are looking for the individual effect of a component of a hidden factor, then you want to control for the factor before measuring the effect of the component, but when measuring the effect of the hidden factor you don’t control for the components. But height isn’t a component of the IQ calculation, though it does appear to be weakly related to intelligence.
I’m also having trouble remembering if the 0.05 number I remembered was an r or r^2, which would significantly impact the range. I’m finding rs of about .2 for the correlation between height and intelligence, and rs of about .2 for the correlation between height and income without controlling for intelligence. Haven’t found anything yet that does control for it. (IQ correlation with income, as mentioned in a cousin comment, is about .4.)
It’s still not clear to me what about the saying you find objectionable. The best guess I have is that you took it to mean that the g-loading of life success was comparable to Raven’s, when I meant that they were g-loaded at all. The heart of that paragraph:
seems to me like a fair explanation of the saying, and why it’s confused to think about nurture factors influencing intelligence as separate from actual intelligence.