Peter H. Schönemann
Professor Emeritus • Department of Psychological Sciences • Purdue University

Abstract 81

[81]

Schonemann, Peter H.

Models and muddles of heritability

Genetica, 1997, 99, 97-108

Abstract

One reason for the astonishing persistence of the IQ myth in the face of overwhelming prior and posterior odds against it may be the unbroken chain of excessive heritability claims for 'intelligence', which IQ tests are supposed to 'measure'. However, if, as some critics insist, 'intelligence' is undefined, and Spearman's g is beset with numerous problems, not the least of which is universal rejection of Spearman's model by the data, then how can the heritability of 'intelligence' exceed that of milk production of cows and egg production of hens?

The thesis of the present review paper is that the answer to this riddle has two parts: (a) the technical basis of heritability claims for human behavior is just as shaky as that of Spearman's g. For example, a once widely used 'heritability estimate' turns out to be mathematically invalid, while another such estimate, though mathematically valid, never fits any data; and (b) valid technical criticisms of flawed heritability claims typically are met with stubborn editorial resistence in the main strream journals, which tends to calcify such misinformation.

Notes

Based on a talk entitled "Totems of  the IQ myth: General Ability g and its Heritabilites (h2, HR)", delivered at the 1995 Meetings of the American Association for the Advancement of Sciences. Among other things, it is shown that the conventional heritability estimates  often produce absurdly high values for variables that cannot possibly be genetic. For example, if one applies the traditional heritability arithmetic to the twin data collected by Loehlin and Nichols (1976), one finds that answers to the question "Did you take a bubble bath last year" are 90% genetic.  This should have alerted the experts long ago that something must be amiss. What is wrong, of course, is that their simplistic models rarely fit the data.

Nontechnical.