The regression argument should’ve been dropped by 1980 at the latest. In fairness to Flynn, his book does namecheck that argument and explain why it’s wrong, albeit only briefly.
If I remember correctly, Loehlin’s book also mentions it briefly. However, it seems to me that the situation is actually more complex.
Jensen’s arguments, in the forms in which he has been stating them for decades, are clearly inadequate. Some very good responses were published 30+ years ago by Mackenzie and Furby. Yet for some bizarre reason, prominent critics of Jensen have typically ignored these excellent references and instead produced their own much less thorough and clear counterarguments.
Nevertheless, I’m not sure if the argument should end here. Certainly, if we observe a subpopulation S in which the values of a trait follow a normal distribution with the mean M(S) that is lower than for the whole population, then in pairs of individuals from S among whom there exists a correlation independent of rank and smaller than one, the lower-ranked individuals will regress towards M(S). That’s a mathematical tautology, and nothing can be inferred from it about what the causes of the individual and group differences might be; the above cited papers explain this fact very well.
However, the question that I’m not sure about is: what can we conclude from the fact that the existing statistical distributions and correlations are such that they satisfy these mathematical conditions? Is this really a trivial consequence of the norming of tests that’s engineered so as to give their scores a normal distribution over the whole population? I’d like to see someone really statistics-savvy scrutinize the issue without starting from the assumption that both the total population distribution and the subpopulation distribution are normal and that the correlation coefficients between relatives are independent of their rank in the distribution.
satt:
If I remember correctly, Loehlin’s book also mentions it briefly. However, it seems to me that the situation is actually more complex.
Jensen’s arguments, in the forms in which he has been stating them for decades, are clearly inadequate. Some very good responses were published 30+ years ago by Mackenzie and Furby. Yet for some bizarre reason, prominent critics of Jensen have typically ignored these excellent references and instead produced their own much less thorough and clear counterarguments.
Nevertheless, I’m not sure if the argument should end here. Certainly, if we observe a subpopulation S in which the values of a trait follow a normal distribution with the mean M(S) that is lower than for the whole population, then in pairs of individuals from S among whom there exists a correlation independent of rank and smaller than one, the lower-ranked individuals will regress towards M(S). That’s a mathematical tautology, and nothing can be inferred from it about what the causes of the individual and group differences might be; the above cited papers explain this fact very well.
However, the question that I’m not sure about is: what can we conclude from the fact that the existing statistical distributions and correlations are such that they satisfy these mathematical conditions? Is this really a trivial consequence of the norming of tests that’s engineered so as to give their scores a normal distribution over the whole population? I’d like to see someone really statistics-savvy scrutinize the issue without starting from the assumption that both the total population distribution and the subpopulation distribution are normal and that the correlation coefficients between relatives are independent of their rank in the distribution.