While Rushton (1999) demonstrates, using PCA, that g and black-white differences were related, with Flynn Effect (FE) gains over time showing no relationship with the aforementioned variables, Flynn (2000) has challenged Rushton in arguing that Wechsler’s subtest loadings on the Raven test, an universally recognized measure of fluid g, showed positive correlations with both black-white differences and FE gains. Up to now, Flynn’s estimates of g fluid (Gf) has not been scrutinized. I will show presently that the Flynn’s g-fluid (call it, fluid reasoning) and Rushton’s g-crystallized (call it, consolidated knowledge) anomaly was solely due to a single statistical artifact, namely, g_Fluid vector unreliability. By adding additional samples, I created a new, updated Wechsler’s subtest Gf loadings. The present analysis comes to the conclusion that g_Fluid was not in fact correlated with FE gains. Furthermore, this Gf variable has been correlated with other variables as well, such as, heritability (h2), shared environment (c2), nonshared environment (e2), adoption IQ gains, inbreeding depression (ID), and mental retardation (MR). I will also discuss these findings in light of Kan’s (2011) thesis against the hereditarian hypothesis.
In his classic work, Educability and Group Differences, Arthur Jensen presented a number of lines of evidence in defense of his thesis that the Negro-White difference in psychometric intelligence had a congenital component. On the basis of full sibling correlations and relations, Jensen offered the following arguments:
(a1) The full sibling correlations for Blacks and Whites are comparable; (a2) unshared environmental hypotheses, such as nutritional ones, would predict otherwise (pg. 338-339).
(b1) The full sibling correlations for Blacks and Whites are comparable; (b2) a shared environmental hypothesis of group differences would predict otherwise, assuming that the within population heritablities were the same (pg. 108-109).
(c1) The average absolute difference between full siblings is no greater for Blacks than for Whites; (c2) unshared environmental hypotheses, such as nutritional ones, would predict otherwise (pg. 338-339).
(d1) When matching Blacks and Whites on IQ, one sees differential sibling regression, a differential regression which does not decrease with increasing IQ; (d2) an environmental hypothesis of group differences would not predict this (pg. 118-119). Continue reading
In The g Factor, Jensen (1998, pp. 384-385) states that because races differ in SES levels, the Spearman-Jensen effect (i.e., g-loading correlates) found in racial IQ differences (hispanics, denoted H; blacks, denoted B; whites, denoted W) could simply reflect this fact. One reason seems to be that SES correlates with g-loadings although he affirms that it was irrelevant to Spearman’s hypothesis (furthermore, this does not necessarily imply that IQ gain due to SES improvement is itself g-loaded; see Jensen 1997, or Metzen 2012). When testing this hypothesis anyway, it was shown that the WISC subtests’ correlation with SES is correlated with WISC g-loading in both the white and black samples. Also, when matching for SES, the BW difference still correlates strongly with g-loadings. Presently, I will try to replicate this result.
Hu (2013, September, 5; 2013, July, 5; 2013, August, 18) has raised some interesting points. I will comment on a few of them here and present several new analyses.
Cultural Loading, Heritability, and the BW gap
As Meng Hu noted, Kan et al. (2011) showed that subtest cultural-loadings, as they estimated them, correlated both with the magnitude of the B/W subtest gaps and with subtest heritability estimates. The authors interpreted these associations as support for a GxE hypothesis of individual differences and offered a model similar to that proposed by Flynn and Dickens (2001). Moreover, Kan et al. (2011) saw the associations between cultural-load and heritability and between cultural-load and the magnitude of the BW gap as problematic for what they termed a biological g model. Below, I will show that g-loadings fully mediate the association between cultural loadings and the two other variables noted and therefore that what is in need of explanation is only the association between cultural-loadings and g-loadings. I will then proceed to offer an account for this.
First, I looked to see if g-loadings mediated the association between the BW gap and cultural loadings. They did. Then I looked to see if cultural-loadings mediated the association between the BW gap and g-loadings. They did not fully. The results are shown below. As reliability estimates were not presented for all subtests, I ran the analysis with and without reliability corrections. Continue reading
Studies of the nature of the Flynn Effect are usually done in developed countries (e.g., Rushton, 1999; Wicherts, 2004; Nijenhuis, 2007; for an ‘Overview of the Flynn Effect’, see Williams, 2013). There are two recent data on two developing countries (Khaleefa, 2009; Liu, 2012). The reported numbers on subtests gains can be studied using either MCV or PC analysis. Next, we will see that shared (c²) and non-shared (e²) environments, as measured by Falconer’s formula, are unrelated to heritability (h²) of the WAIS and WISC subtests. Culture load, heritability, g-loadings, and black-white differences tend to form a common cluster (on PC1) that is different from the pattern of loadings shown by shared and non-shared environment.
At what age does the cognitive ability gap between blacks and whites first appear? At what age does the black-white ability gap stop growing?
Knowing the answers to these questions is vital to understanding the etiology of the black-white ability gap, especially if this gap has an environmental cause. However, the only scholarly work that attempts to investigate these issues is John Loehlin’s Race Differences in Intelligence (1975), which is nearly 40 years old. So I will update and expand upon that review here on Human Varieties by summarizing all available measurements of African American cognitive ability from early infancy to age 3; I will also discuss the relevance of this data to current debates in the social sciences.
In the NLSY97, a Jensen Effect of biracial blacks has been found, using self-reported white ancestry. In the NLSY79, some questionnaires (R00096.00, R00097.00) asked about the respondents’ first and second racial/ethnic origin. When the respondent reported being non-black or white in one of the questionnaires and black in the other, he was categorized as being a multiracial.
Bermuda is a tiny British Overseas Territory in the North Atlantic Ocean, some 600 miles from the East Coast of the United States (population: 64,700). Even though Bermuda is 1000 miles from the Caribbean Sea, there are a number of sociological similarities between Bermuda and the Caribbean island nations; it is an associate member of the Caribbean Community. Its economy, much like the Cayman Islands and The Bahamas, is largely based on finance and tourism, and it likewise enjoys one of the highest standards of living in the world.
According to the 2000 census, Bermuda is 54.8% black and 34.1% white. IQ and the Wealth of Nations (2002) did not include intelligence data for Bermuda, but IQ and Global Inequality (2006) reported an IQ of 90, as the average of two studies. In this post I discuss some overlooked data which suggest that Bermudian blacks have an IQ that is very close to 100, and that there is no IQ gap between black and white Bermudians. There is also some overlooked test data which suggest otherwise, and we are left with some uncertainty over the meaning of the conflicting research. Continue reading
In an earlier article, I have shown that the magnitude of sibling correlations among NLSY-ASVAB subtests correlates with the magnitude of g-loadings, but moderately with the magnitude of black-white IQ gaps in those subtests using Jensen’s method of correlated vectors, a possibly imperfect technique in some instances as explained in my previous article. In another post, it has been seen that US blacks having more (self-reported) white ancestry showed a higher IQ level, and that this effect is not mediated by skin color. Here, I will show that the magnitude of the score advantage for blacks with more white ancestry among subtests correlates with the above mentioned variables.
Number 4 in the social science’s top 10 list of “grand challenge questions that are both foundational and transformative” (Giles, 2010) is: “How do we reduce the ‘skill gap’ between black and white people in America?” Presumably, figuring out the cause of this psychometric intelligence differential would help when it comes to deciding how best to minimize it. If so, we can thank Meng Hu for his recent efforts focused on determining the cause. This includes his recent extensive exploration of differential regression.