In this analysis of the Project Talent data, the g factor model as represented by the Spearman’s Hypothesis (SH) was confirmed for the black-white cognitive difference but not for the sex difference. Results from MGCFA and MCV corroborate each other. MGCFA detected small-modest bias with respect to race but strong bias with respect to sex cognitive difference. Full result is available at OSF.
Multivariate genetic analyses and simple correlational analyses have been conducted to evaluate the extent to which the general factor (g) of intelligence is differentially heritable, compared to, for example, group factors. A positive correlation would be supportive of Jensen’s view, notably advanced in The g Factor (1998), of the heritable g. This can be interpreted to say that what makes people being good at all tests has more genetic component than what make people being good at one specific test. On the other hand, if environmental effects are smaller at the g level, it would mean that what make people being good at all tests has less environmental component than what make people good at one specific test. Similarly, if heritability is large at the g level and environment is small at the g level, then g differences between persons are largely genetic, not environmental (Plomin, 2003, p. 186).
The present article is a review of the studies published so far and can be seen as a complement to my article on the genetics of intelligence. Brody (2007) and Deary (2006) have already reviewed a large part of the existing studies. But some features need to be highlighted. The article can be subjected to modifications if I happen to read some more studies not listed here (I prefer not to publish a new article each time I read a new research paper). Shortly, there seems to be some proof of differential heritabilities, higher for g. But it’s not overwhelming.
The present analysis is an extension of Spitz’s earlier (1988) study on the relationship between mental retardation (MR) lower score and subtest heritability (h2) and g-loadings. These relationships were found to be positive. But Spitz himself haven’t tested the possibility that MR (lower) score could be related with shared (c2) or nonshared (e2) environment. I use the WAIS and WISC data given in my earlier post, and have found that MR is not related with c2 and e2 values. These findings nevertheless must be interpreted very carefully because the small number of subtests (e.g., 10 or 11) is a very critical limitation.
I analyze two studies who provide the necessary data for studying the test-retest effects, namely, Watkins (2007), Schellenberg (2004, 2006). Both used the Wechsler’s subtests, and the correlations between the IQ changes among those subtests with g-loadings are negative, in line with earlier studies on this topic.
A correlation between the g factor and indices of heritability (h2) gives support for the genetic g hypothesis but, on the other hand, the interpretation may appear questionable if g correlates with shared (c2) and/or non shared (e2) environment to the same extent. The results from the present meta-analysis tend to support the hereditarian hypothesis.
The french adoption study, by Capron & Duyme (1989, Table 2; 1996, Table 3), attempted to show that IQ can be improved by adoption. Their numbers displayed an IQ gain of 15 or even 20 points (WISC-R). To recall, Jensen (1997) analyzed Capron and Duyme adoption data (1996) with N=38, a study often cited by environmentalists as evidence against the hereditarian hypothesis. In Adoption Data and Two g-Related Hypotheses, Jensen shows that IQ difference owing to the adoption of children from low-SES parents by high-SES families is not g-loaded while at the same time the IQ difference owing to low-SES versus high-SES biological families loaded in fact on the g factor or PC1. Plus, the SES-difference of adopted families correlated at only 0.099 with SES-difference of biological families.