Okay I read Jayman’s method more closely and I see that he did filter for respondents who were at least 42 years old at the time of responding. This is similar to what I wanted to do by drawing only from years where the youngest of the cohort is old enough to have a stabilized CHILDS score. Except that Jayman’s way oversamples from the oldest of the cohort because 1969 babies only had the GSS year of 2011 when they were old enough to pass this age filter, whereas 1960 babies had GSS years of 2002-2011 when they were old enough to pass the survey. So the oversampling makes it not perfect but it’s hardly a “serious” flaw.

I redid the analysis again using Jayman’s age filtering and only GSS years up to 2011 (which I think is what was available when the original blog post was written in 2014). I also filtered for RACE(1) like Jayman did. Here is the result.

Cohorts | total correlation | M | F

1960-1969 | +0.01 | +0.08 | -0.08

So I did nearly replicate Jayman’s result using his exact method which wasn’t “seriously” flawed, but now with GSS years available up to 2016 and using a different filter to draw years as in my first comment, the result is not replicated.

]]>I redid your analysis with this restriction and got the following result. It’s negative all the way down:

Cohorts | total correlation | M | F

1883-1909 | -0.14 | -0.16 | -0.13

1910-1919 | -0.07 | -0.03 | -0.10

1920-1929 | -0.11 | -0.11 | -0.11

1930-1939 | -0.12 | -0.09 | -0.15

1940-1949 | -0.13 | -0.08 | -0.16

1950-1959 | -0.11 | -0.05 | -0.16

1960-1969 | -0.10 | -0.09 | -0.11

1970-1979 | NO DATA

Gah, you’re still not getting my point. I’ll try one more framing.

Consider three scenarios:

A. A randomly-selected subpopulation with IQ 130 breaks away from the main population (which has IQ 100) and has kids. You tell us these kids should have an average IQ of 118.

B. A randomly-selected subpopulation with IQ 130 who came from families with a mean IQ of 100 break off and have kids. You tell us (in the section about predicting IQ for individuals) that each individual couple in this subpopulation should expect their kids to have an IQ of 118.

C. A randomly-selected subpopulation with IQ 130 who came from families with a mean IQ of 118 break off and have kids. Based on the same calculation as we did for scenario B, each individual couple in this subpopulation should expect their kids to have an IQ of 125.

Scenario A is the subpopulation scenario you describe in the post. But for the conclusion that the next generation will have a mean IQ of 118 to be true, scenario A would have to be the same as scenario B; the people in the subpopulation that breaks away would have to come from families with a mean IQ of 100.

Yet, as you note yourself in the comments above, that’s not true! Scenario A is in fact *the same scenario as scenario C* – as you note above, a random person with IQ 130 from a population with mean IQ 100 will on average have come from a family with a mean IQ of 118! So we’re left with a paradox: our calculation is scenario A tells us that the next generation in the subpopulation should have a mean IQ of 118, but our calculation in identical scenario C tells us that each individual in that next generation should have a mean IQ of 125. These can’t both be true.

If you double select, that’s what would happen. If you apply it one time, you get 118.

If you then take the 130+ kids from the next generation and repeat the process, you get 125.2.

]]>But if the people in our 130-IQ subpop come from families with a mean IQ of 118, then they’re only 12 IQ (0.8 SD) above their family mean, so the breeder’s equation tells us their kids should have

0.6 • 0.8 σ = +0.6667σ = +7.2 points,

which implies their expected IQ should be 118+7.2 = 125.2

That’s the inconsistency I’m pointing to – this calculation comes out at 125 points, whereas the result you got from plugging population stats into equation was 118; one of them has to be wrong.

]]>If we randomly take people with 130 IQ from the starting population (that had mean IQ 100), the mean family IQ of the families those people belong to isn’t going to be 100; it’s going to lie somewhere between 100 and 130.

Indeed. And you know what that number is in this instance? 118.

That’s what regression is: non-additive factors going away/averaging out so that the true genetic potential value comes through.

]]>Consider again your hypothetical involving a subpopulation branching off and having kids. Namely:

a group of people with a mean IQ of 130 (who come from population with a mean IQ of 100) go off somewhere and have children

Given the above (and that 30 IQ points is 2 standard deviations), you tell us that it’s legit to plug in h² = 0.6 and S=2 into the breeder’s equation and compute a next-generation expected IQ of 1.2 SD above the mean, or 118. Okay.

But earlier you told us we could do the same calculation on the per-family level. If we apply the family-level breeder’s equation to each family in the subpopulation, what result do we get? Well, we can suppose that every individual in the 130-IQ subpopulation has 130 IQ and came from a family with a mean family IQ of 100; then the breeder’s equation would tell us that each individual’s offspring will have an expected IQ of 118, which is consistent with the result from the population-level application of the equation.

But here’s the catch: the assumption of a mean family IQ of 100 can’t be correct here. If we randomly take people with 130 IQ from the starting population (that had mean IQ 100), the mean family IQ of the families those people belong to isn’t going to be 100; it’s going to lie somewhere between 100 and 130. And, as you noted earlier, if you increase the family mean while holding the parental IQ constant, then the expected IQ of the children goes up. So the per-family application of the breeder’s equation should *actually* predict the children’s IQs to be some value *greater* than 118.

There’s therefore a contradiction here; the result we get from applying the equation to a population in the way you suggest we can do is not the same as the result we’d get if we applied it to every family in that population individually, as you *also* suggest we can do. Which means that one of these applications must be invalid.

http://raconteurreport.blogspot.com/2020/03/welcome.html?m=1

Meanwhile in Japan, they let some of their own citizens from the Diamond Princess go home on the train.

It’s extremely contagious, as demonstated by the one woman who spread it in South Korea.

There will be a pandemic. Governments just seem to be trying to slow it down a bit.

]]>Yes, all that. It can still be avoided now but in a few weeks it’ll be too late.

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